Global robust adaptive path following of underactuated ships
机器人顶刊论文
机器人顶刊论文机器人领域内除开science robotics以外,TRO和IJRR是机器人领域的两大顶刊,最近师弟在选择研究方向,因此对两大顶刊的论文做了整理。
TRO的全称IEEE Transactions on Robotics,是IEEE旗下机器人与自动化协会的汇刊,最新的影响因子为6.123。
ISSUE 61 An End-to-End Approach to Self-Folding Origami Structures2 Continuous-Time Visual-Inertial Odometry for Event Cameras3 Multicontact Locomotion of Legged Robots4 On the Combined Inverse-Dynamics/Passivity-Based Control of Elastic-Joint Robots5 Control of Magnetic Microrobot Teams for Temporal Micromanipulation Tasks6 Supervisory Control of Multirotor Vehicles in Challenging Conditions Using Inertial Measurements7 Robust Ballistic Catching: A Hybrid System Stabilization Problem8 Discrete Cosserat Approach for Multisection Soft Manipulator Dynamics9 Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System10 Multimodal Sensorimotor Integration for Expert-in-the-Loop Telerobotic Surgical Training11 Fast, Generic, and Reliable Control and Simulation of Soft Robots Using Model Order Reduction12 A Path/Surface Following Control Approach to Generate Virtual Fixtures13 Modeling and Implementation of the McKibben Actuator in Hydraulic Systems14 Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving15 Robust Planar Odometry Based on Symmetric Range Flow and Multiscan Alignment16 Accelerated Sensorimotor Learning of Compliant Movement Primitives17 Clock-Torqued Rolling SLIP Model and Its Application to Variable-Speed Running in aHexapod Robot18 On the Covariance of X in AX=XB19 Safe Testing of Electrical Diathermy Cutting Using a New Generation Soft ManipulatorISSUE 51 Toward Dexterous Manipulation With Augmented Adaptive Synergies: The Pisa/IIT SoftHand 22 Efficient Equilibrium Testing Under Adhesion and Anisotropy Using Empirical Contact Force Models3 Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification4 An Ankle–Foot Prosthesis Emulator With Control of Plantarflexion and Inversion–Eversion Torque5 SLAP: Simultaneous Localization and Planning Under Uncertainty via Dynamic Replanning in Belief Space6 An Analytical Loading Model for n -Tendon Continuum Robots7 A Direct Dense Visual Servoing Approach Using Photometric Moments8 Computational Design of Robotic Devices From High-Level Motion Specifications9 Multicontact Postures Computation on Manifolds10 Stiffness Modulation in an Elastic Articulated-Cable Leg-Orthosis Emulator: Theory and Experiment11 Human–Robot Communications of Probabilistic Beliefs via a Dirichlet Process Mixture of Statements12 Multirobot Reconnection on Graphs: Problem, Complexity, and Algorithms13 Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras14 Reactive Trajectory Generation for Multiple Vehicles in Unknown Environments With Wind Disturbances15 Resource-Aware Large-Scale Cooperative Three-Dimensional Mapping Using Multiple Mobile Devices16 Control of Planar Spring–Mass Running Through Virtual Tuning of Radial Leg Damping17 Gait Design for a Snake Robot by Connecting Curve Segments and ExperimentalDemonstration18 Server-Assisted Distributed Cooperative Localization Over Unreliable Communication Links19 Realization of Smooth Pursuit for a Quantized Compliant Camera Positioning SystemISSUE 41 A Survey on Aerial Swarm Robotics2 Trajectory Planning for Quadrotor Swarms3 A Distributed Control Approach to Formation Balancing and Maneuvering of Multiple Multirotor UAVs4 Joint Coverage, Connectivity, and Charging Strategies for Distributed UAV Networks5 Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle6 Agile Coordination and Assistive Collision Avoidance for Quadrotor Swarms Using Virtual Structures7 Decentralized Trajectory Tracking Control for Soft Robots Interacting With the Environment8 Resilient, Provably-Correct, and High-Level Robot Behaviors9 Humanoid Dynamic Synchronization Through Whole-Body Bilateral Feedback Teleoperation10 Informed Sampling for Asymptotically Optimal Path Planning11 Robust Tactile Descriptors for Discriminating Objects From Textural Properties via Artificial Robotic Skin12 VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator13 Zero Step Capturability for Legged Robots in Multicontact14 Fast Gait Mode Detection and Assistive Torque Control of an Exoskeletal Robotic Orthosis for Walking Assistance15 Physically Plausible Wrench Decomposition for Multieffector Object Manipulation16 Considering Uncertainty in Optimal Robot Control Through High-Order Cost Statistics17 Multirobot Data Gathering Under Buffer Constraints and Intermittent Communication18 Image-Guided Dual Master–Slave Robotic System for Maxillary Sinus Surgery19 Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes20 Periodic Trajectory Planning Beyond the Static Workspace for 6-DOF Cable-Suspended Parallel Robots1 Computationally Efficient Trajectory Generation for Fully Actuated Multirotor Vehicles2 Aural Servo: Sensor-Based Control From Robot Audition3 An Efficient Acyclic Contact Planner for Multiped Robots4 Dimensionality Reduction for Dynamic Movement Primitives and Application to Bimanual Manipulation of Clothes5 Resolving Occlusion in Active Visual Target Search of High-Dimensional Robotic Systems6 Constraint Gaussian Filter With Virtual Measurement for On-Line Camera-Odometry Calibration7 A New Approach to Time-Optimal Path Parameterization Based on Reachability Analysis8 Failure Recovery in Robot–Human Object Handover9 Efficient and Stable Locomotion for Impulse-Actuated Robots Using Strictly Convex Foot Shapes10 Continuous-Phase Control of a Powered Knee–Ankle Prosthesis: Amputee Experiments Across Speeds and Inclines11 Fundamental Actuation Properties of Multirotors: Force–Moment Decoupling and Fail–Safe Robustness12 Symmetric Subspace Motion Generators13 Recovering Stable Scale in Monocular SLAM Using Object-Supplemented Bundle Adjustment14 Toward Controllable Hydraulic Coupling of Joints in a Wearable Robot15 Geometric Construction-Based Realization of Spatial Elastic Behaviors in Parallel and Serial Manipulators16 Dynamic Point-to-Point Trajectory Planning Beyond the Static Workspace for Six-DOF Cable-Suspended Parallel Robots17 Investigation of the Coin Snapping Phenomenon in Linearly Compliant Robot Grasps18 Target Tracking in the Presence of Intermittent Measurements via Motion Model Learning19 Point-Wise Fusion of Distributed Gaussian Process Experts (FuDGE) Using a Fully Decentralized Robot Team Operating in Communication-Devoid Environment20 On the Importance of Uncertainty Representation in Active SLAM1 Robust Visual Localization Across Seasons2 Grasping Without Squeezing: Design and Modeling of Shear-Activated Grippers3 Elastic Structure Preserving (ESP) Control for Compliantly Actuated Robots4 The Boundaries of Walking Stability: Viability and Controllability of Simple Models5 A Novel Robotic Platform for Aerial Manipulation Using Quadrotors as Rotating Thrust Generators6 Dynamic Humanoid Locomotion: A Scalable Formulation for HZD Gait Optimization7 3-D Robust Stability Polyhedron in Multicontact8 Cooperative Collision Avoidance for Nonholonomic Robots9 A Physics-Based Power Model for Skid-Steered Wheeled Mobile Robots10 Formation Control of Nonholonomic Mobile Robots Without Position and Velocity Measurements11 Online Identification of Environment Hunt–Crossley Models Using Polynomial Linearization12 Coordinated Search With Multiple Robots Arranged in Line Formations13 Cable-Based Robotic Crane (CBRC): Design and Implementation of Overhead Traveling Cranes Based on Variable Radius Drums14 Online Approximate Optimal Station Keeping of a Marine Craft in the Presence of an Irrotational Current15 Ultrahigh-Precision Rotational Positioning Under a Microscope: Nanorobotic System, Modeling, Control, and Applications16 Adaptive Gain Control Strategy for Constant Optical Flow Divergence Landing17 Controlling Noncooperative Herds with Robotic Herders18 ε⋆: An Online Coverage Path Planning Algorithm19 Full-Pose Tracking Control for Aerial Robotic Systems With Laterally Bounded Input Force20 Comparative Peg-in-Hole Testing of a Force-Based Manipulation Controlled Robotic HandISSUE 11 Development of the Humanoid Disaster Response Platform DRC-HUBO+2 Active Stiffness Tuning of a Spring-Based Continuum Robot for MRI-Guided Neurosurgery3 Parallel Continuum Robots: Modeling, Analysis, and Actuation-Based Force Sensing4 A Rationale for Acceleration Feedback in Force Control of Series Elastic Actuators5 Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration6 Interaction Between Inertia, Viscosity, and Elasticity in Soft Robotic Actuator With Fluidic Network7 Exploiting Elastic Energy Storage for “Blind”Cyclic Manipulation: Modeling, Stability Analysis, Control, and Experiments for Dribbling8 Enhance In-Hand Dexterous Micromanipulation by Exploiting Adhesion Forces9 Trajectory Deformations From Physical Human–Robot Interaction10 Robotic Manipulation of a Rotating Chain11 Design Methodology for Constructing Multimaterial Origami Robots and Machines12 Dynamically Consistent Online Adaptation of Fast Motions for Robotic Manipulators13 A Controller for Guiding Leg Movement During Overground Walking With a Lower Limb Exoskeleton14 Direct Force-Reflecting Two-Layer Approach for Passive Bilateral Teleoperation With Time Delays15 Steering a Swarm of Particles Using Global Inputs and Swarm Statistics16 Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints17 A Three-Dimensional Magnetic Tweezer System for Intraembryonic Navigation and Measurement18 Adaptive Compensation of Multiple Actuator Faults for Two Physically Linked 2WD Robots19 General Lagrange-Type Jacobian Inverse for Nonholonomic Robotic Systems20 Asymmetric Bimanual Control of Dual-Arm Exoskeletons for Human-Cooperative Manipulations21 Fourier-Based Shape Servoing: A New Feedback Method to Actively Deform Soft Objects into Desired 2-D Image Contours22 Hierarchical Force and Positioning Task Specification for Indirect Force Controlled Robots。
全球气候变化对农业生产的影响与适应考研英语作文范文
全球气候变化对农业生产的影响与适应考研英语作文范文Climate Change and Its Impact on Agricultural Production Climate change has emerged as one of the most pressing challenges facing our planet today. Its impact onagricultural production is significant and far-reaching, necessitating urgent action and adaptive strategies. This essay will discuss the various ways in which global climate change affects agricultural productivity and explorepotential solutions to mitigate its adverse effects.Firstly, rising temperatures due to climate change have profound implications for agricultural systems. Heat stress can reduce crop yields, particularly for heat-sensitive crops such as wheat and rice. Increased temperatures alsocontribute to the spread of pests and diseases, furtherendangering agricultural productivity. Moreover, extreme heat events can lead to livestock deaths and decreased milk and meat production. These adverse effects put food security and livelihoods at risk, particularly in regions dependent on agriculture.Secondly, altered precipitation patterns resulting from climate change pose another major challenge for agriculture. Changes in rainfall timing, frequency, and intensity can disrupt planting and harvesting schedules, affecting crop growth and yields. Droughts, often exacerbated by climate change, reduce soil moisture and hinder plant growth, leading to crop failures. Conversely, heavy precipitation events can cause waterlogging and soil erosion, damaging crops and causing nutrient depletion. These unpredictable rainfall patterns increase the vulnerability of agricultural systems and exacerbate food insecurity.Furthermore, climate change impacts the availability and quality of water resources, essential for agricultural irrigation. Melting glaciers and shifting rainfall patterns affect the quantity of available water, leading to water scarcity in many regions. This scarcity is exacerbated by increased evaporation rates due to higher temperatures. Additionally, rising temperatures contribute to the degradation of water quality, affecting soil fertility and crop health. These challenges necessitate innovative water management strategies and investments in efficient irrigation technologies.Another significant consequence of climate change for agriculture is the risk of increased extreme weather events. More frequent and intense hurricanes, cyclones, and storms can cause widespread destruction of crops, buildings, and infrastructure. Flooding, accompanied by soil erosion, destroys fertile topsoil and reduces agriculturalproductivity in affected areas. Similarly, heatwaves anddroughts can lead to forest fires, damaging land and crops. Building resilience through improved disaster risk management, early warning systems, and robust infrastructure is crucialfor enabling farmers to adapt to the changing climate.To adapt to the challenges posed by climate change, farmers and policymakers must embrace sustainableagricultural practices. Implementing climate-smartagriculture techniques, such as conservation tillage, agroforestry, and integrated pest management, can enhance resilience and reduce greenhouse gas emissions. Furthermore, promoting crop diversification and breeding climate-resilient varieties can help mitigate the effects of extreme weather conditions. Developing climate information services and providing financial support and capacity-buildingopportunities for farmers is also essential.In conclusion, climate change significantly impacts agricultural production through increased temperatures,altered precipitation patterns, water scarcity, and extreme weather events. Addressing these challenges requires a combination of mitigation and adaptation efforts. Sustainable agricultural practices, crop diversification, and the development of climate-resilient varieties are crucial. Additionally, investing in water management strategies and disaster risk reduction measures is necessary for building resilience in agricultural systems. By taking proactive measures, we can safeguard food security and livelihoods in the face of a changing climate.。
滑模变结构控制理论及其算法研究与进展
第24卷第3期2007年6月控制理论与应用Control Theory&ApplicationsV ol.24No.3Jun.2007滑模变结构控制理论及其算法研究与进展刘金琨1,孙富春2(1.北京航空航天大学自动化与电气工程学院,北京100083;2.清华大学智能技术与系统国家重点实验室,北京100084)摘要:针对近年来滑模变结构控制的发展状况,将滑模变结构控制分为18个研究方向,即滑模控制的消除抖振问题、准滑动模态控制、基于趋近律的滑模控制、离散系统滑模控制、自适应滑模控制、非匹配不确定性系统滑模控制、时滞系统滑模控制、非线性系统滑模控制、Terminal滑模控制、全鲁棒滑模控制、滑模观测器、神经网络滑模控制、模糊滑模控制、动态滑模控制、积分滑模控制和随机系统的滑模控制等.对每个方向的研究状况进行了分析和说明.最后对滑模控制的未来发展作了几点展望.关键词:滑模控制;鲁棒控制;抖振中图分类号:TP273文献标识码:AResearch and development on theory and algorithms ofsliding mode controlLIU Jin-kun1,SUN Fu-chun2(1.School of Automation Science&Electrical Engineering,Beijing University of Aeronautics and Astronautics,Beijing100083,China;2.State Key Laboratory of Intelligent Technology and Systems,Tsinghua University,Beijing100084,China)Abstract:According to the development of sliding mode control(SMC)in recent years,the SMC domain is character-ized by eighteen directions.These directions are chattering free of SMC,quasi SMC,trending law SMC,discrete SMC, adaptive SMC,SMC for mismatched uncertain systems,SMC for nonlinear systems,time-delay SMC,terminal SMC, global robust SMC,sliding mode observer,neural SMC,fuzzy SMC,dynamic SMC,integral SMC and SMC for stochastic systems,etc.The evolution of each direction is introduced and analyzed.Finally,further research directions are discussed in detail.Key words:sliding mode control;robust control;chattering文章编号:1000−8152(2007)03−0407−121引言(Introduction)滑模变结构控制本质上是一类特殊的非线性控制,其非线性表现为控制的不连续性,这种控制策略与其它控制的不同之处在于系统的“结构”并不固定,而是可以在动态过程中根据系统当前的状态(如偏差及其各阶导数等)有目的地不断变化,迫使系统按照预定“滑动模态”的状态轨迹运动.由于滑动模态可以进行设计且与对象参数及扰动无关,这就使得变结构控制具有快速响应、对参数变化及扰动不灵敏、无需系统在线辩识,物理实现简单等优点.该方法的缺点在于当状态轨迹到达滑模面后,难于严格地沿着滑模面向着平衡点滑动,而是在滑模面两侧来回穿越,从而产生颤动.滑模变结构控制出现于20世纪50年代,经历了50余年的发展,已形成了一个相对独立的研究分支,成为自动控制系统的一种一般的设计方法.以滑模为基础的变结构控制系统理论经历了3个发展阶段.第1阶段为以误差及其导数为状态变量研究单输入单输出线性对象的变结构控制;20世纪60年代末开始了变结构控制理论研究的第2阶段,研究的对象扩大到多输入多输出系统和非线性系统;进入80年代以来,随着计算机、大功率电子切换器件、机器人及电机等技术的迅速发展,变结构控制的理论和应用研究开始进入了一个新的阶段,所研究的对象已涉及到离散系统、分布参数系统、滞后系统、非线性大系统及非完整力学系统等众多复杂系统,同时,自适应控制、神经网络、模糊控制及遗传算法等先进方法也被应用于滑模变结构控制系统的设计中.2滑模变结构控制理论研究进展(Develop-ment for SMC)2.1消除滑模变结构控制抖振的方法研究(Research on chattering elimination of SMC) 2.1.1滑模变结构控制的抖振问题(Problems ofSMC chattering)从理论角度,在一定意义上,由于滑动模态可以收稿日期:2005−10−19;收修改稿日期:2006−02−23.基金项目:国家自然科学基金资助项目(60474025,90405017).408控制理论与应用第24卷按需要设计,而且系统的滑模运动与控制对象的参数变化和系统的外干扰无关,因此滑模变结构控制系统的鲁棒性要比一般常规的连续系统强.然而,滑模变结构控制在本质上的不连续开关特性将会引起系统的抖振.对于一个理想的滑模变结构控制系统,假设“结构”切换的过程具有理想开关特性(即无时间和空间滞后),系统状态测量精确无误,控制量不受限制,则滑动模态总是降维的光滑运动而且渐近稳定于原点,不会出现抖振.但是对于一个现实的滑模变结构控制系统,这些假设是不可能完全成立的.特别是对于离散系统的滑模变结构控制系统,都将会在光滑的滑动模态上叠加一个锯齿形的轨迹.于是,在实际上,抖振是必定存在的,而且消除了抖振也就消除了变结构控制的抗摄动和抗扰动的能力,因此,消除抖振是不可能的,只能在一定程度上削弱它到一定的范围.抖振问题成为变结构控制在实际系统中应用的突出障碍.抖振产生的主要原因有:①时间滞后开关:在切换面附近,由于开关的时间滞后,控制作用对状态的准确变化被延迟一定的时间;又因为控制量的幅度是随着状态量的幅度逐渐减少的,所以表现为在光滑的滑动模台上叠加一个衰减的三角波.②空间滞后开关:开关滞后相当于在状态空间中存在一个状态量变化的“死区”.因此,其结果是在光滑的滑模面上叠加了一个等幅波形.③系统惯性的影响:由于任何物理系统的能量不可能是无限大,因而系统的控制力不能无限大,这就使系统的加速度有限;另外,系统惯性总是存在的,所以使得控制切换伴有滞后,这种滞后与时间滞后效果相同.④离散系统本身造成的抖振:离散系统的滑动模态是一种“准滑动模态”,它的切换动作不是正好发生在切换面上,而是发生在以原点为顶点的一个锥形体的表面上.因此有衰减的抖振,而且锥形体越大,则抖振幅度越大.该锥形体的大小与采样周期有关.总之,抖振产生的原因在于:当系统的轨迹到达切换面时,其速度是有限大,惯性使运动点穿越切换面,从而最终形成抖振,叠加在理想的滑动模态上.对于实际的计算机采样系统而言,计算机的高速逻辑转换以及高精度的数值运算使得切换开关本身的时间及空间滞后影响几乎不存在,因此,开关的切换动作所造成控制的不连续性是抖振发生的本质原因.在实际系统中,由于时间滞后开关、空间滞后开关、系统惯性、系统延迟及测量误差等因素,使变结构控制在滑动模态下伴随着高频振动,抖振不仅影响控制的精确性、增加能量消耗,而且系统中的高频未建模动态很容易被激发起来,破坏系统的性能,甚至使系统产生振荡或失稳,损坏控制器部件.因此,关于控制信号抖振消除的研究成为变结构控制研究的首要工作.2.1.2消除滑模变结构控制抖振的几种方法(Several methods for eliminating chatteringin SMC)国内外针对滑模控制抗抖振问题的研究很多,许多学者都从不同的角度提出了解决方法.目前这些方法主要有:1)滤波方法.通过采用滤波器,对控制信号进行平滑滤波,是消除抖振的有效方法.文[1]为了消除离散滑模控制的抖振,设计了两种滤波器:前滤波器和后滤波器,其中前滤波器用于控制信号的平滑及缩小饱和函数的边界层厚度,后滤波器用于消除对象输出的噪声干扰.文[2]在边界层内,对切换函数采用了低通滤波器,得到平滑的信号,并采用了内模原理,设计了一种新型的带有积分和变边界层厚度的饱和函数,有效地降低了抖振.文[3]利用机器人的物理特性,通过在控制器输出端加入低通滤波器,设计了虚拟滑模控制器,实现了机器人全鲁棒变结构控制,并保证了系统的稳定,有效地消除了抖振.文[4]设计了带有滤波器的变结构控制器,有效地消除了控制信号的抖振,得到了抑制高频噪声的非线性控制器,实现了存在非建模动态的电液伺服马达的定位控制.文[5]为了克服未建模动态特性造成的滑动模态抖振,设计了一种新型滑模控制器,该控制器输出通过一个二阶滤波器,实现控制器输出信号的平滑,其中辅助滑动模面的系数通过滑模观测器得到.文[6]提出了一种新型控制律,即,该控制律由3部分构成,即等效控制、切换控制和连续控制,在控制律中采用了两个低通滤波器,其中通过一个低通滤波器得到切换项的增益,通过另一个低通滤波器得到等效控制项,并进行了收敛性和稳定性分析,有效地抑制了抖振,实现了多关节机器手的高性能控制.2)消除干扰和不确定性的方法.在常规滑模控制中,往往需要很大的切换增益来消除外加干扰及不确定项,因此,外界干扰及不确定项是滑模控制中抖振的主要来源.利用观测器来消除外界干扰及不确定性成为解决抖振问题研究的重点.文[7]为了将常规滑模控制方法应用于带有较强强外加干扰的伺服系统中,设计了一种新型干第3期刘金琨等:滑模变结构控制理论及其算法研究与进展409扰观测器,通过对外加干扰的前馈补偿,大大地降低了滑模控制器中切换项的增益,有效地消除了抖振.文[8]在滑模控制中设计了一种基于二元控制理论的干扰观测器,将观测到的干扰进行前馈补偿,减小了抖振.文[9]提出了一种基于误差预测的滑模控制方法,在该方法中设计了一种观测器和滤波器,通过观测器消除了未建模动态的影响,采用均值滤波器实现了控制输入信号的平滑,有效地消除了未建模动态造成的抖振.文[10]设计了一种离散的滑模观测器,实现了对控制输入端干扰的观测,从而实现对干扰的有效补偿,相对地减小了切换增益.3)遗传算法优化方法.遗传算法是建立在自然选择和自然遗传学机理基础上的迭代自适应概率性搜索算法,在解决非线性问题时表现出很好的鲁棒性、全局最优性、可并行性和高效率,具有很高的优化性能.文[11]针对非线性系统设计了一种软切换模糊滑模控制器,采用遗传算法对该控制器增益参数及模糊规则进行离线优化,有效地减小了控制增益,从而消除了抖振.针对不确定性伺服系统设计了一种积分自适应滑模控制器,通过该控制器中的自适应增益项来消除不确定性及外加干扰,如果增益项为常数,则会造成抖振,为此,文[12]设计了一种实时遗传算法,实现了滑模变结构控制器中自适应增益项的在线自适应优化,有效地减小了抖振.文[13]采用遗传算法进行切换函数的优化,将抖振的大小作为优化适应度函数的重要指标,构造一个抖振最小的切换函数.4)降低切换增益方法.由于抖振主要是由于控制器的不连续切换项造成,因此,减小切换项的增益,便可有效地消除抖振.文[14]根据滑模控制的Lypunov稳定性要求,设计了时变的切换增益,减小了抖振.文[15]对切换项进行了变换,通过设计一个自适应积分项来代替切换项,实现了切换项增益的自适应调整,有效地减小了切换项的增益.文[16]针对一类带有未建模动态系统的控制问题,提出了一种鲁棒低增益变结构模型参考自适应控制新方法,使系统在含未建模动态时所有辅助误差均可在有限时间内收敛为零,并保证在所有情况下均为低增益控制.文[17]提出了采用模糊神经网络的切换增益自适应调节算法,当跟踪误差接近于零时,切换增益接近于零,大大降低了抖振.5)扇形区域法.文[18]针对不确定非线性系统,设计了包含两个滑动模面的滑动扇区,构造连续切换控制器使得在开关面上控制信号是连续的.文[19]采用滑动扇区法,在扇区之内采用连续的等效控制,在扇区之外采用趋近律控制,很大程度地消除了控制的抖振.6)其他方法.文[20]针对滑模变结构控制中引起抖振的动态特性,将抖振看成叠加在理想滑模上的有限频率的振荡,提出了滑动切换面的优化设计方法,即通过切换面的设计,使滑动模态的频率响应具有某种希望的形状,实现频率整形.该频率整形能够抑制滑动模态中引起抖振的频率分量,使切换面为具有某种“滤波器”特性的动态切换面.文[21]设计了一种能量函数,该能量函数包括控制精度和控制信号的大小,采用LMI(linear matrix inequality)方法设计滑动模面,使能量函数达到最小,实现了滑动模面的优化,提高了控制精度,消除了抖振.2.2准滑动模态滑模控制(Quasi-sliding modecontrol)80年代在滑动模态控制的设计中引入了“准滑动模态”和“边界层”的概念[22],实现准滑动模态控制,采用饱和函数代替切换函数,即在边界层以外采用正常的滑模控制,在边界层内为连续状态的反馈控制,有效地避免或削弱了抖振,为变结构控制的工程应用开辟了道路.此后,有许多学者对于切换函数和边界层的设计进行了研究.①连续函数近似法.文[23]采用Sigmoid连续函数来代替切换函数.文[24]针对直流电机伺服系统的未建模动态进行了分析和描述,设计了基于插补平滑算法的滑模控制器,实现了非连续切换控制的连续化,有效地消除了未建模动态对直流电机伺服系统造成的抖振.②边界层的设计.边界层厚度越小,控制效果越好,但同时又会使控制增益变大,抖振增强;反之,边界层厚度越大,抖振越小,但又会使控制增益变小,控制效果差.为了获得最佳抗抖振效果,边界层厚度应自适应调整.文[25]提出了一种高增益滑模控制器,设控制信号输入为u,切换函数为s(t),将|˙u|作为衡量抖振的指标,按降低控制抖振来设计模糊规则,将|s|和|˙u|作为模糊规则的输入,模糊推理的输出为边界层厚度的变化,实现了边界层厚度的模糊自适应调整.文[26]针对不确定性线性系统,同时考虑了控制信号的降抖振与跟踪精度的要求,提出了一种基于系统状态范数的边界层厚度在线调整算法.文[27]提出了一种新型的动态滑模控制,采用饱和函数方法,通过设计一种新型非线性切换函数,消除了滑模到达阶段的抖振,实现了全局鲁棒滑模控制,有效地解决了一类非线性机械系统的控制抖振问题.文[28]为了减小边界层厚度,在边界层内采用了积分控制,既获得了稳态误差,又避免了抖振.边界层的方法仅能保410控制理论与应用第24卷证系统状态收敛到以滑动面为中心的边界层内,只能通过较窄的边界层来任意地接近滑模,但不能使状态收敛到滑模.2.3基于趋近律的滑模控制(Sliding mode controlbased on trending law)高为炳利用趋近律的概念,提出了一种变结构控制系统的抖振消除方法[29].以指数趋近律˙s=−ε·sgn s−k·s为例,通过调整趋近律的参数κ和ε,既可以保证滑动模态到达过程的动态品质,又可以减弱控制信号的高频抖振,但较大的ε值会导致抖振.文[30]分析了指数趋近律应用于离散系统时趋近系数造成抖振的原因,并对趋近系数与抖振的关系进行了定量的分析,提出了趋近系数ε的自适应调整算法.文[31]提出了将离散趋近律与等效控制相结合的控制策略,离散趋近律仅在趋近阶段起作用,当系统状态到达准滑模模态阶段,采用了抗干扰的离散等效控制,既保证了趋近模态具有良好品质,又降低了准滑动模态带,消除了抖振.文[32]将模糊控制应用于指数趋近律中,通过分析切换函数与指数趋近律中系数的模糊关系,利用模糊规则调节指数趋近律的系数,其中切换函数的绝对值|s|作为模糊规则的输入,指数趋近律的系数κ和ε作为模糊规则的输出,使滑动模态的品质得到了进一步的改善,消除了系统的高频抖振.2.4离散系统滑模变结构控制(Sliding mode con-trol for discrete system)连续时间系统和离散时间系统的控制有很大差别.自80年代初至今,由于计算机技术的飞速发展,实际控制中使用的都是离散系统,因此,对离散系统的变结构控制研究尤为重要.对离散系统变结构控制的研究是从80年代末开始的,例如,Sarpturk等于1987年提出了一种新型离散滑模到达条件,在此基础上又提出了离散控制信号必须是有界的理论[33],Furuta于1990年提出了基于等效控制的离散滑模变结构控制[34],高为炳于1995年提出了基于趋近律的离散滑模变结构控制[35].他们各自提出的离散滑模变结构滑模存在条件及其控制方法已被广泛应用.然而,传统设计方法存在两方面不足:一是由于趋近律自身参数及切换开关的影响,即使对名义系统,系统状态轨迹也只能稳定于原点邻域的某个抖振;二是由于根据不确定性上下界进行控制器设计,可能会造成大的反馈增益,使控制抖振加剧.近年来国内外学者一方面对离散系统滑模变结构控制的研究不断深入.文[36]提出了基于PR型的离散系统滑模面设计方法,其中P和R分别为与系统状态有关的正定对称阵和半正定对称阵,在此基础上设计了稳定的离散滑模控制器,通过适当地设计P和R,保证了控制器具有良好的性能.文[37]针对离散系统提出了一种新型滑模存在条件,进一步拓展了离散滑模控制的设计,在此基础上设计了一种新型滑模控制律.针对离散系统中滑模控制的不变性和鲁棒性难以有效保证,文[38]提出了3种解决方法,在第1种方法中,采用了干扰补偿器和解耦器消除干扰,在第2种方法中,采用回归切换函数方法来消除干扰,在第3种方法中,采用回归切换函数和解耦器相结合的方法来消除干扰,上述3种方法已成功地应用于数控中.文[39]针对数字滑模控制的鲁棒性进行了系统的研究,提出了高增益数字滑模控制器.文[40]针对带有干扰和未知参数的多输入多输出离散系统的滑模控制进行了研究,并采用自适应律实现了未知项的估计.2.5自适应滑模变结构控制(Adaptive slidingmode control)自适应滑模变结构控制是滑模变结构控制与自适应控制的有机结合,是一种解决参数不确定或时变参数系统控制问题的一种新型控制策略.文[41]针对线性化系统将自适应Backsteping与滑模变结构控制设计方法结合在一起,实现了自适应滑模变结构控制,文[42]针对一类最小相位的可线性化的非线性系统,设计了一种动态自适应变结构控制器,实现了带有不确定性和未知外干扰的非线性系统鲁棒控制.在一般的滑模变结构控制中,为了保证系统能够达到切换面,在设计控制律时通常要求系统不确定性范围的界已知,这个要求在实际工程中往往很难达到,针对具有未知参数变化和干扰变化的不确定性系统的变结构控制,文[43]设计了一种新型的带有积分的滑动模面,并采用一种自适应滑模控制方法,控制器的设计无需不确定性及外加干扰的上下界,实现了一类不确定伺服系统的自适应变结构控制.针对自适应滑模控制中参数估计值无限增大的缺点,文[44]提出了一种新的参数自适应估计方法,保证了变结构控制增益的合理性.近年来,变结构模型参考自适应控制理论取得了一系列重要进展,由于该方法具有良好的过渡过程性能和鲁棒性,在工程上得到了很好的应用.文[45]设计了一种新型动态滑动模面,滑动模面参数通过采用自适应算法估计得到,从而实现了非线性系统的模型参考自适应滑模控制.文[46]针对一类不确定性气压式伺服系统,提出了模型参考自适应滑模控制方法,并在此基础上提出了克服控制抖振的有效方法.第3期刘金琨等:滑模变结构控制理论及其算法研究与进展4112.6非匹配不确定性系统的滑模变结构控制(Sliding mode control for systems with mis-matched uncertainties)由于大多数系统不满足变结构控制的匹配条件,因此,存在非匹配不确定性系统的变结构控制是一个研究重点.文[47]利用参数自适应控制方法,构造了一个变参数的切换函数,对具有非匹配不确定性的系统进行了变结构控制设计.采用基于线性矩阵不等式LMI的方法,为非匹配不确定性系统的变结构控制提供了新的思路,Choi针对不匹配不确定性系统,专门研究了利用LMI方法进行变结构控制设计的问题[48∼50].Backstepping设计方法通过引入中间控制器,使控制器的设计系统化、程序化,它对于非匹配不确定性系统及非最小相位系统的变结构控制是一种十分有效的方法.采用Backstepping设计方法,文[51]实现了对于一类具有非匹配不确定性的非线性系统的变结构控制.将Backstepping设计方法、滑模控制及自适应方法相结合,文[52]实现了一类具有非匹配不确定性的非线性系统的自适应滑模控制.2.7针对时滞系统的滑模变结构控制(Slidingmode control for time-delay system)由于实际系统普遍存在状态时滞、控制变量时滞,因此,研究具有状态或控制时滞系统的变结构控制,对进一步促进变结构控制理论的应用具有重要意义.文[53]对于具有输入时滞的不确定性系统,通过状态变换的方法,实现了滑模变结构控制器的设计.文[54]研究了带有关联时滞项的大系统的分散模型跟踪变结构控制问题,其中被控对象的时滞关联项必须满足通常的匹配条件.文[55]采用趋近律的方法设计了一种新型控制器,采用了基于LMI的方法进行了稳定性分析和切换函数的设计,所设计的控制器保证了对非匹配不确定性和匹配的外加干扰具有较强的鲁棒性,解决了非匹配参数不确定性时滞系统的变结构控制问题.文[56]针对带有输出延迟非线性系统的滑模控制器的设计进行了探讨,在该方法中,将延迟用一阶Pade近似的方法来代替,并将非最小相位系统转化为稳定系统,在存在未建模动态和延迟不确定性条件下,控制器获得了很好的鲁棒性能.国内在时滞系统的滑模变结构控制方面也取得了许多成果,针对时滞系统的变结构控制器设计问题和时滞变结构控制系统的理论问题进行了多年的研究,取得了许多成果[57∼59].2.8非线性系统的滑模变结构控制(Sliding modecontrol for nonlinear system)非线性系统的滑模变结构控制一直是人们关注的热点.文[60]研究了具有正则形式的非线性系统的变结构控制问题,为非线性系统变结构控制理论的发展奠定了基础.目前,非最小相位非线性系统、输入受约束非线性系统、输入和状态受约束非线性系统等复杂问题的变结构控制是该领域研究的热点.文[61]将Anti-windup方法与滑模控制方法相结合,设计了输入饱和的Anti-windup算法,实现当输出为饱和时的高精度变结构控制,文[62]利用滑模变结构控制方法实现了一类非最小相位非线性系统的鲁棒控制,文[63]利用输入输出反馈线性化、相对度、匹配条件等非线性系统的概念,采用输出反馈变结构控制方法实现了一类受约束非线性系统的鲁棒输出跟踪反馈控制.文[64]利用Backstepping方法,实现了非线性不确定性系统的变结构控制.2.9Terminal滑模变结构控制(Terminal slidingmode control)在普通的滑模控制中,通常选择一个线性的滑动超平面,使系统到达滑动模态后,跟踪误差渐进地收敛为零,并且渐进收敛的速度可以通过选择滑模面参数矩阵任意调节.尽管如此,无论如何状态跟踪误差都不会在有限时间内收敛为零.近年来,为了获得更好的性能,一些学者提出了一种Terminal(终端)滑模控制策略[65∼67],该策略在滑动超平面的设计中引入了非线性函数,使得在滑模面上跟踪误差能够在有限时间内收敛到零.Ter-minal滑模控制是通过设计一种动态非线性滑模面方程实现的,即在保证滑模控制稳定性的基础上,使系统状态在指定的有限时间内达到对期望状态的完全跟踪.例如,文[68]将动态非线性滑模面方程设计为s=x2+βx q/p1,其中p>q,p和q为正的奇数,β>0.但该控制方法由于非线性函数的引入使得控制器在实际工程中实现困难,而且如果参数选取不当,还会出现奇异问题.文[69]探讨了非奇异Termianl滑模控制器的设计问题,并针对N自由度刚性机器人的控制进行了验证.文[70]采用模糊规则设计了Terminal滑模控制器的切换项,并通过自适应算法对切换项增益进行自适应模糊调节,实现了非匹配不确定性时变系统的Terminal滑模控制,同时降低了抖阵.文[71]中只对一个二阶系统给出了相应的Terminal滑模面,滑模面的导数是不连续的,不适用于高阶系统.文[72]设计了一种适用于高阶非线性系统的Terminal滑模面,克服了文[71]中的滑模面导数不连续的缺点,并消除了滑模控制的到达阶段,确保了系统的全局鲁棒性和稳定性,进一步地,庄开宇等[73]又针对系统参数摄动和外界扰动等不确定性因素上界的未知性,实现了MIMO系统的自适应Terminal控制器设计,所设计的滑模面方程既保。
车辆控制系统说明书
IndexAactuation layer, 132average brightness,102-103adaptive control, 43Badaptive cruise control, 129backpropagation algorithm, 159adaptive FLC, 43backward driving mode,163,166,168-169adaptive neural networks,237adaptive predictive model, 283Baddeley-Molchanov average, 124aerial vehicles, 240 Baddeley-Molchanov fuzzy set average, 120-121, 123aerodynamic forces,209aerodynamics analysis, 208, 220Baddeley-Molchanov mean,118,119-121alternating filter, 117altitude control, 240balance position, 98amplitude distribution, 177bang-bang controller,198analytical control surface, 179, 185BCFPI, 61-63angular velocity, 92,208bell-shaped waveform,25ARMAX model, 283beta distributions,122artificial neural networks,115Bezier curve, 56, 59, 63-64association, 251Bezier Curve Fuzzy PI controller,61attitude angle,208, 217Bezier function, 54aumann mean,118-120bilinear interpolation, 90, 300,302automated manual transmission,145,157binary classifier,253Bo105 helicopter, 208automatic formation flight control,240body frame,238boiler following mode,280,283automatic thresholding,117border pixels, 101automatic transmissions,145boundary layer, 192-193,195-198autonomous robots,130boundary of a fuzzy set,26autonomous underwater vehicle, 191braking resistance, 265AUTOPIA, 130bumpy control surface, 55autopilot signal, 228Index 326CCAE package software, 315, 318 calibration accuracy, 83, 299-300, 309, 310, 312CARIMA models, 290case-based reasoning, 253center of gravity method, 29-30, 32-33centroid defuzzification, 7 centroid defuzzification, 56 centroid Method, 106 characteristic polygon, 57 characterization, 43, 251, 293 chattering, 6, 84, 191-192, 195, 196, 198chromosomes, 59circuit breaker, 270classical control, 1classical set, 19-23, 25-26, 36, 254 classification, 106, 108, 111, 179, 185, 251-253classification model, 253close formation flight, 237close path tracking, 223-224 clustering, 104, 106, 108, 251-253, 255, 289clustering algorithm, 252 clustering function, 104clutch stroke, 147coarse fuzzy logic controller, 94 collective pitch angle, 209 collision avoidance, 166, 168 collision avoidance system, 160, 167, 169-170, 172collision avoidance system, 168 complement, 20, 23, 45 compressor contamination, 289 conditional independence graph, 259 confidence thresholds, 251 confidence-rated rules, 251coning angle, 210constant gain, 207constant pressure mode, 280 contrast intensification, 104 contrast intensificator operator, 104 control derivatives, 211control gain, 35, 72, 93, 96, 244 control gain factor, 93control gains, 53, 226control rules, 18, 27, 28, 35, 53, 64, 65, 90-91, 93, 207, 228, 230, 262, 302, 304-305, 315, 317control surfaces, 53-55, 64, 69, 73, 77, 193controller actuator faulty, 289 control-weighting matrix, 207 convex sets, 119-120Coordinate Measurement Machine, 301coordinate measuring machine, 96 core of a fuzzy set, 26corner cube retroreflector, 85 correlation-minimum, 243-244cost function, 74-75, 213, 282-283, 287coverage function, 118crisp input, 18, 51, 182crisp output, 7, 34, 41-42, 51, 184, 300, 305-306crisp sets, 19, 21, 23crisp variable, 18-19, 29critical clearing time, 270 crossover, 59crossover probability, 59-60cruise control, 129-130,132-135, 137-139cubic cell, 299, 301-302, 309cubic spline, 48cubic spline interpolation, 300 current time gap, 136custom membership function, 294 customer behav or, 249iDdamping factor, 211data cleaning, 250data integration, 250data mining, 249, 250, 251-255, 259-260data selection, 250data transformation, 250d-dimensional Euclidean space, 117, 124decision logic, 321 decomposition, 173, 259Index327defuzzification function, 102, 105, 107-108, 111 defuzzifications, 17-18, 29, 34 defuzzifier, 181, 242density function, 122 dependency analysis, 258 dependency structure, 259 dependent loop level, 279depth control, 202-203depth controller, 202detection point, 169deviation, 79, 85, 185-188, 224, 251, 253, 262, 265, 268, 276, 288 dilation, 117discriminated rules, 251 discrimination, 251, 252distance function, 119-121 distance sensor, 167, 171 distribution function, 259domain knowledge, 254-255 domain-specific attributes, 251 Doppler frequency shift, 87 downhill simplex algorithm, 77, 79 downwash, 209drag reduction, 244driver’s intention estimator, 148 dutch roll, 212dynamic braking, 261-262 dynamic fuzzy system, 286, 304 dynamic tracking trajectory, 98Eedge composition, 108edge detection, 108 eigenvalues, 6-7, 212electrical coupling effect, 85, 88 electrical coupling effects, 87 equilibrium point, 207, 216 equivalent control, 194erosion, 117error rates, 96estimation, 34, 53, 119, 251, 283, 295, 302Euler angles, 208evaluation function, 258 evolution, 45, 133, 208, 251 execution layer, 262-266, 277 expert knowledge, 160, 191, 262 expert segmentation, 121-122 extended sup-star composition, 182 Ffault accommodation, 284fault clearing states, 271, 274fault detection, 288-289, 295fault diagnosis, 284fault durations, 271, 274fault isolation, 284, 288fault point, 270-271, 273-274fault tolerant control, 288fault trajectories, 271feature extraction, 256fiber glass hull, 193fin forces, 210final segmentation, 117final threshold, 116fine fuzzy controller, 90finer lookup table, 34finite element method, 318finite impulse responses, 288firing weights, 229fitness function, 59-60, 257flap angles, 209flight aerodynamic model, 247 flight envelope, 207, 214, 217flight path angle, 210flight trajectory, 208, 223footprint of uncertainty, 176, 179 formation geometry, 238, 247 formation trajectory, 246forward driving mode, 163, 167, 169 forward flight control, 217 forward flight speed, 217forward neural network, 288 forward velocity, 208, 214, 217, 219-220forward velocity tracking, 208 fossil power plants, 284-285, 296 four-dimensional synoptic data, 191 four-generator test system, 269 Fourier filter, 133four-quadrant detector, 79, 87, 92, 96foveal avascular zone, 123fundus images, 115, 121, 124 fuselage, 208-210Index 328fuselage axes, 208-209fuselage incidence, 210fuzz-C, 45fuzzifications, 18, 25fuzzifier, 181-182fuzzy ACC controller, 138fuzzy aggregation operator, 293 fuzzy ASICs, 37-38, 50fuzzy binarization algorithm, 110 fuzzy CC controller, 138fuzzy clustering algorithm, 106, 108 fuzzy constraints, 286, 291-292 fuzzy control surface, 54fuzzy damage-mitigating control, 284fuzzy decomposition, 108fuzzy domain, 102, 106fuzzy edge detection, 111fuzzy error interpolation, 300, 302, 305-306, 309, 313fuzzy filter, 104fuzzy gain scheduler, 217-218 fuzzy gain-scheduler, 207-208, 220 fuzzy geometry, 110-111fuzzy I controller, 76fuzzy image processing, 102, 106, 111, 124fuzzy implication rules, 27-28 fuzzy inference system, 17, 25, 27, 35-36, 207-208, 302, 304-306 fuzzy interpolation, 300, 302, 305- 307, 309, 313fuzzy interpolation method, 309 fuzzy interpolation technique, 300, 309, 313fuzzy interval control, 177fuzzy mapping rules, 27fuzzy model following control system, 84fuzzy modeling methods, 255 fuzzy navigation algorithm, 244 fuzzy operators, 104-105, 111 fuzzy P controller, 71, 73fuzzy PD controller, 69fuzzy perimeter, 110-111fuzzy PI controllers, 61fuzzy PID controllers, 53, 64-65, 80 fuzzy production rules, 315fuzzy reference governor, 285 Fuzzy Robust Controller, 7fuzzy set averages, 116, 124-125 fuzzy sets, 7, 19, 22, 24, 27, 36, 45, 115, 120-121, 124-125, 151, 176-182, 184-188, 192, 228, 262, 265-266fuzzy sliding mode controller, 192, 196-197fuzzy sliding surface, 192fuzzy subsets, 152, 200fuzzy variable boundary layer, 192 fuzzyTECH, 45Ggain margins, 207gain scheduling, 193, 207, 208, 211, 217, 220gas turbines, 279Gaussian membership function, 7 Gaussian waveform, 25 Gaussian-Bell waveforms, 304 gear position decision, 145, 147 gear-operating lever, 147general window function, 105 general-purpose microprocessors, 37-38, 44genetic algorithm, 54, 59, 192, 208, 257-258genetic operators, 59-60genetic-inclined search, 257 geometric modeling, 56gimbal motor, 90, 96global gain-scheduling, 220global linear ARX model, 284 global navigation satellite systems, 141global position system, 224goal seeking behaviour, 186-187 governor valves80, 2HHamiltonian function, 261, 277 hard constraints, 283, 293 heading angle, 226, 228, 230, 239, 240-244, 246heading angle control, 240Index329heading controller, 194, 201-202 heading error rate, 194, 201 heading speed, 226heading velocity control, 240 heat recovery steam generator, 279 hedges, 103-104height method, 29helicopter, 207-212, 214, 217, 220 helicopter control matrix, 211 helicopter flight control, 207 Heneghan method, 116-117, 121-124heuristic search, 258 hierarchical approaches, 261 hierarchical architecture, 185 hierarchical fuzzy processors, 261 high dimensional systems, 191 high stepping rates, 84hit-miss topology, 119home position, 96horizontal tail plane, 209 horizontal tracker, 90hostile, 223human domain experts, 255 human visual system, 101hybrid system framework, 295 hyperbolic tangent function, 195 hyperplane, 192-193, 196 hysteresis thres olding, 116-117hIIF-THEN rule, 27-28image binarization, 106image complexity, 104image fuzzification function, 111 image segmentation, 124image-expert, 122-123indicator function, 121inert, 223inertia frame, 238inference decision methods, 317 inferential conclusion, 317 inferential decision, 317 injection molding process, 315 inner loop controller, 87integral time absolute error, 54 inter-class similarity, 252 internal dependencies, 169 interpolation property, 203 interpolative nature, 262 intersection, 20, 23-24, 31, 180 interval sets, 178interval type-2 FLC, 181interval type-2 fuzzy sets, 177, 180-181, 184inter-vehicle gap, 135intra-class similarity, 252inverse dynamics control, 228, 230 inverse dynamics method, 227 inverse kinema c, 299tiJ - Kjoin, 180Kalman gain, 213kinematic model, 299kinematic modeling, 299-300 knowledge based gear position decision, 148, 153knowledge reasoning layer, 132 knowledge representation, 250 knowledge-bas d GPD model, 146eLlabyrinths, 169laser interferometer transducer, 83 laser tracker, 301laser tracking system, 53, 63, 65, 75, 78-79, 83-85, 87, 98, 301lateral control, 131, 138lateral cyclic pitch angle, 209 lateral flapping angle, 210 leader, 238-239linear control surface, 55linear fuzzy PI, 61linear hover model, 213linear interpolation, 300-301, 306-307, 309, 313linear interpolation method, 309 linear optimal controller, 207, 217 linear P controller, 73linear state feedback controller, 7 linear structures, 117linear switching line, 198linear time-series models, 283 linguistic variables, 18, 25, 27, 90, 102, 175, 208, 258Index 330load shedding, 261load-following capabilities, 288, 297 loading dock, 159-161, 170, 172 longitudinal control, 130-132 longitudinal cyclic pitch angle, 209 longitudinal flapping angle, 210 lookup table, 18, 31-35, 40, 44, 46, 47-48, 51, 65, 70, 74, 93, 300, 302, 304-305lower membership functions, 179-180LQ feedback gains, 208LQ linear controller, 208LQ optimal controller, 208LQ regulator, 208L-R fuzzy numbers, 121 Luenburger observer, 6Lyapunov func on, 5, 192, 284tiMMamdani model, 40, 46 Mamdani’s method, 242 Mamdani-type controller, 208 maneuverability, 164, 207, 209, 288 manual transmissions, 145 mapping function, 102, 104 marginal distribution functions, 259 market-basket analysis, 251-252 massive databases, 249matched filtering, 115 mathematical morphology, 117, 127 mating pool, 59-60max member principle, 106max-dot method, 40-41, 46mean distance function, 119mean max membership, 106mean of maximum method, 29 mean set, 118-121measuring beam, 86mechanical coupling effects, 87 mechanical layer, 132median filter, 105meet, 7, 50, 139, 180, 183, 302 membership degree, 39, 257 membership functions, 18, 25, 81 membership mapping processes, 56 miniature acrobatic helicopter, 208 minor steady state errors, 217 mixed-fuzzy controller, 92mobile robot control, 130, 175, 181 mobile robots, 171, 175-176, 183, 187-189model predictive control, 280, 287 model-based control, 224 modeless compensation, 300 modeless robot calibration, 299-301, 312-313modern combined-cycle power plant, 279modular structure, 172mold-design optimization, 323 mold-design process, 323molded part, 318-321, 323 morphological methods, 115motor angular acceleration, 3 motor plant, 3motor speed control, 2moving average filter, 105 multilayer fuzzy logic control, 276 multimachine power system, 262 multivariable control, 280 multivariable fuzzy PID control, 285 multivariable self-tuning controller, 283, 295mutation, 59mutation probability, 59-60mutual interference, 88Nnavigation control, 160neural fuzzy control, 19, 36neural networks, 173, 237, 255, 280, 284, 323neuro-fuzzy control, 237nominal plant, 2-4nonlinear adaptive control, 237non-linear control, 2, 159 nonlinear mapping, 55nonlinear switching curve, 198-199 nonlinear switching function, 200 nonvolatile memory, 44 normalized universe, 266Oobjective function, 59, 74-75, 77, 107, 281-282, 284, 287, 289-291,Index331295obstacle avoidance, 166, 169, 187-188, 223-225, 227, 231 obstacle avoidance behaviour, 187-188obstacle sensor, 224, 228off-line defuzzification, 34off-line fuzzy inference system, 302, 304off-line fuzzy technology, 300off-line lookup tables, 302 offsprings, 59-60on-line dynamic fuzzy inference system, 302online tuning, 203open water trial, 202operating point, 210optical platform, 92optimal control table, 300optimal feedback gain, 208, 215-216 optimal gains, 207original domain, 102outer loop controller, 85, 87outlier analysis, 251, 253output control gains, 92 overshoot, 3-4, 6-7, 60-61, 75-76, 94, 96, 193, 229, 266Ppath tracking, 223, 232-234 pattern evaluation, 250pattern vector, 150-151PD controller, 4, 54-55, 68-69, 71, 74, 76-77, 79, 134, 163, 165, 202 perception domain, 102 performance index, 60, 207 perturbed plants, 3, 7phase margins, 207phase-plan mapping fuzzy control, 19photovoltaic power systems, 261 phugoid mode, 212PID, 1-4, 8, 13, 19, 53, 61, 64-65, 74, 80, 84-85, 87-90, 92-98, 192 PID-fuzzy control, 19piecewise nonlinear surface, 193 pitch angle, 202, 209, 217pitch controller, 193, 201-202 pitch error, 193, 201pitch error rate, 193, 201pitch subsidence, 212planetary gearbox, 145point-in-time transaction, 252 polarizing beam-splitter, 86 poles, 4, 94, 96position sensor detectors, 84 positive definite matrix, 213post fault, 268, 270post-fault trajectory, 273pre-defined membership functions, 302prediction, 251, 258, 281-283, 287, 290predictive control, 280, 282-287, 290-291, 293-297predictive supervisory controller, 284preview distance control, 129 principal regulation level, 279 probabilistic reasoning approach, 259probability space, 118Problem understanding phases, 254 production rules, 316pursuer car, 136, 138-140 pursuer vehicle, 136, 138, 140Qquadrant detector, 79, 92 quadrant photo detector, 85 quadratic optimal technology, 208 quadrilateral ob tacle, 231sRradial basis function, 284 random closed set, 118random compact set, 118-120 rapid environment assessment, 191 reference beam, 86relative frame, 240relay control, 195release distance, 169residual forces, 217retinal vessel detection, 115, 117 RGB band, 115Riccati equation, 207, 213-214Index 332rise time, 3, 54, 60-61, 75-76road-environment estimator, 148 robot kinematics, 299robot workspace, 299-302, 309 robust control, 2, 84, 280robust controller, 2, 8, 90robust fuzzy controller, 2, 7 robustness property, 5, 203roll subsidence, 212rotor blade flap angle, 209rotor blades, 210rudder, 193, 201rule base size, 191, 199-200rule output function, 191, 193, 198-199, 203Runge-Kutta m thod, 61eSsampling period, 96saturation function, 195, 199 saturation functions, 162scaling factor, 54, 72-73scaling gains, 67, 69S-curve waveform, 25secondary membership function, 178 secondary memberships, 179, 181 selection, 59self-learning neural network, 159 self-organizing fuzzy control, 261 self-tuning adaptive control, 280 self-tuning control, 191semi-positive definite matrix, 213 sensitivity indices, 177sequence-based analysis, 251-252 sequential quadratic programming, 283, 292sets type-reduction, 184setting time, 54, 60-61settling time, 75-76, 94, 96SGA, 59shift points, 152shift schedule algorithms, 148shift schedules, 152, 156shifting control, 145, 147shifting schedules, 146, 152shift-schedule tables, 152sideslip angle, 210sigmoidal waveform, 25 sign function, 195, 199simplex optimal algorithm, 80 single gimbal system, 96single point mass obstacle, 223 singleton fuzzification, 181-182 sinusoidal waveform, 94, 300, 309 sliding function, 192sliding mode control, 1-2, 4, 8, 191, 193, 195-196, 203sliding mode fuzzy controller, 193, 198-200sliding mode fuzzy heading controller, 201sliding pressure control, 280 sliding region, 192, 201sliding surface, 5-6, 192-193, 195-198, 200sliding-mode fuzzy control, 19 soft constraints, 281, 287space-gap, 135special-purpose processors, 48 spectral mapping theorem, 216 speed adaptation, 138speed control, 2, 84, 130-131, 133, 160spiral subsidence, 212sporadic alternations, 257state feedback controller, 213 state transition, 167-169state transition matrix, 216state-weighting matrix, 207static fuzzy logic controller, 43 static MIMO system, 243steady state error, 4, 54, 79, 90, 94, 96, 98, 192steam turbine, 279steam valving, 261step response, 4, 7, 53, 76, 91, 193, 219stern plane, 193, 201sup operation, 183supervisory control, 191, 280, 289 supervisory layer, 262-264, 277 support function, 118support of a fuzzy set, 26sup-star composition, 182-183 surviving solutions, 257Index333swing curves, 271, 274-275 switching band, 198switching curve, 198, 200 switching function, 191, 194, 196-198, 200switching variable, 228system trajector192, 195y,Ttail plane, 210tail rotor, 209-210tail rotor derivation, 210Takagi-Sugeno fuzzy methodology, 287target displacement, 87target time gap, 136t-conorm maximum, 132 thermocouple sensor fault, 289 thickness variable, 319-320three-beam laser tracker, 85three-gimbal system, 96throttle pressure, 134throttle-opening degree, 149 thyristor control, 261time delay, 63, 75, 91, 93-94, 281 time optimal robust control, 203 time-gap, 135-137, 139-140time-gap derivative, 136time-gap error, 136time-invariant fuzzy system, 215t-norm minimum, 132torque converter, 145tracking error, 79, 84-85, 92, 244 tracking gimbals, 87tracking mirror, 85, 87tracking performance, 84-85, 88, 90, 192tracking speed, 75, 79, 83-84, 88, 90, 92, 97, 287trajectory mapping unit, 161, 172 transfer function, 2-5, 61-63 transient response, 92, 193 transient stability, 261, 268, 270, 275-276transient stability control, 268 trapezoidal waveform, 25 triangular fuzzy set, 319triangular waveform, 25 trim, 208, 210-211, 213, 217, 220, 237trimmed points, 210TS fuzzy gain scheduler, 217TS fuzzy model, 207, 290TS fuzzy system, 208, 215, 217, 220 TS gain scheduler, 217TS model, 207, 287TSK model, 40-41, 46TS-type controller, 208tuning function, 70, 72turbine following mode, 280, 283 turn rate, 210turning rate regulation, 208, 214, 217two-DOF mirror gimbals, 87two-layered FLC, 231two-level hierarchy controllers, 275-276two-module fuzzy logic control, 238 type-0 systems, 192type-1 FLC, 176-177, 181-182, 185- 188type-1 fuzzy sets, 177-179, 181, 185, 187type-1 membership functions, 176, 179, 183type-2 FLC, 176-177, 180-183, 185-189type-2 fuzzy set, 176-180type-2 interval consequent sets, 184 type-2 membership function, 176-178type-reduced set, 181, 183-185type-reduction,83-1841UUH-1H helicopter, 208uncertain poles, 94, 96uncertain system, 93-94, 96 uncertain zeros, 94, 96underlying domain, 259union, 20, 23-24, 30, 177, 180unit control level, 279universe of discourse, 19-24, 42, 57, 151, 153, 305unmanned aerial vehicles, 223 unmanned helicopter, 208Index 334unstructured dynamic environments, 177unstructured environments, 175-177, 179, 185, 187, 189upper membership function, 179Vvalve outlet pressure, 280vapor pressure, 280variable structure controller, 194, 204velocity feedback, 87vertical fin, 209vertical tracker, 90vertical tracking gimbal, 91vessel detection, 115, 121-122, 124-125vessel networks, 117vessel segmentation, 115, 120 vessel tracking algorithms, 115 vision-driven robotics, 87Vorob’ev fuzzy set average, 121-123 Vorob'ev mean, 118-120vortex, 237 WWang and Mendel’s algorithm, 257 WARP, 49weak link, 270, 273weighing factor, 305weighting coefficients, 75 weighting function, 213weld line, 315, 318-323western states coordinating council, 269Westinghouse turbine-generator, 283 wind–diesel power systems, 261 Wingman, 237-240, 246wingman aircraft, 238-239 wingman veloc y, 239itY-ZYager operator, 292Zana-Klein membership function, 124Zana-Klein method, 116-117, 121, 123-124zeros, 94, 96µ-law function, 54µ-law tuning method, 54。
关于人工智能的用途英语作文
关于人工智能的用途英语作文Artificial Intelligence: Shaping the Future of HumanityThe rapid advancements in technology have ushered in a new era of innovation and progress, and at the forefront of this revolution is the field of Artificial Intelligence (AI). AI has the potential to revolutionize various aspects of our lives, from healthcare and education to transportation and communication. As we delve deeper into the realm of AI, it becomes increasingly evident that its applications hold the key to a brighter and more efficient future.One of the most significant applications of AI is in the healthcare industry. AI-powered diagnostic tools can analyze vast amounts of medical data, including patient records, test results, and medical images, to identify patterns and provide early detection of diseases. This not only leads to more accurate and timely diagnoses but also enables healthcare professionals to develop personalized treatment plans tailored to the individual needs of each patient. Furthermore, AI-powered robotic assistants can perform complex surgical procedures with greater precision and reduced risk of complications, improving patient outcomes and reducing recovery times.Another crucial area where AI is making a profound impact is in the field of education. AI-powered adaptive learning systems can analyze a student's learning patterns, strengths, and weaknesses, and then tailor the curriculum and teaching methods to their individual needs. This personalized approach to learning can significantly improve student engagement, retention, and overall academic performance. Additionally, AI-powered virtual tutors and chatbots can provide students with 24/7 access to learning resources and personalized guidance, ensuring that no student is left behind.The transportation sector has also witnessed a significant transformation thanks to the advancements in AI. Self-driving vehicles, powered by AI-powered sensors and decision-making algorithms, have the potential to revolutionize the way we move around. These autonomous vehicles can navigate through traffic, avoid collisions, and optimize routes, ultimately reducing the risk of accidents and improving the efficiency of transportation systems. Furthermore, AI-powered traffic management systems can analyze real-time data from various sources, such as traffic cameras and GPS, to optimize traffic flow and reduce congestion, leading to a more sustainable and environmentally friendly transportation infrastructure.In the realm of communication and information management, AI has become an indispensable tool. AI-powered language translationservices can bridge the gap between people of different linguistic backgrounds, enabling seamless communication and collaboration across the globe. Moreover, AI-powered personal assistants can help individuals manage their schedules, set reminders, and automate various tasks, freeing up time and mental resources for more productive pursuits.Beyond these practical applications, AI also holds the potential to address some of the most pressing global challenges. For instance, AI-powered climate modeling and forecasting can help us better understand the impact of climate change and develop more effective strategies for mitigation and adaptation. Similarly, AI-powered systems can be used to analyze and process vast amounts of data related to poverty, hunger, and disease, enabling policymakers and humanitarian organizations to develop more targeted and effective interventions.However, as we embrace the transformative power of AI, it is crucial to address the ethical and societal implications of its widespread adoption. Concerns around privacy, data security, and the potential displacement of human labor by AI-powered automation must be carefully considered and addressed. It is essential to develop robust ethical frameworks and regulatory guidelines to ensure that the development and deployment of AI technologies are aligned with the values and well-being of society.In conclusion, the applications of Artificial Intelligence are vast and far-reaching, with the potential to revolutionize various aspects of our lives. From healthcare and education to transportation and communication, AI is poised to shape the future of humanity in profound and transformative ways. As we continue to harness the power of AI, it is our responsibility to ensure that its development and implementation are guided by ethical principles and a commitment to the betterment of society as a whole.。
模型参考自适应控制与鲁棒自适应控制比较
模型参考自适应控制与鲁棒自适应控制比较自适应控制是一种常见的控制策略,旨在使系统能够自动调整控制参数以适应不确定性和变化的环境。
在自适应控制中,模型参考自适应控制(Model Reference Adaptive Control,简称MRAC)和鲁棒自适应控制(Robust Adaptive Control,简称RAC)是两种常用的方法。
本文将对这两种自适应控制方法进行比较分析。
一、模型参考自适应控制模型参考自适应控制是一种基于模型参考的自适应控制方法。
它通过引入一个模型参考器,将期望输出与实际输出进行比较,然后根据比较结果对控制参数进行在线调整。
模型参考自适应控制的主要思想是通过使用与被控对象相似的模型来进行控制,从而提高系统的鲁棒性和跟踪性能。
模型参考自适应控制的主要优点是能够实现对系统模型误差的自适应校正,具有较好的系统鲁棒性和跟踪精度。
该方法在理论上是可行的,并已经在一些实际控制系统中得到了应用。
然而,模型参考自适应控制也存在一些局限性,比如对模型的要求较高、对系统参数的连续性和可观测性要求较严格等。
二、鲁棒自适应控制鲁棒自适应控制是一种能够处理系统不确定性和外部干扰的自适应控制方法。
它通过设计鲁棒控制器来使系统具有鲁棒性,同时引入自适应机制对控制参数进行在线调整。
鲁棒自适应控制的关键在于设计合适的鲁棒控制器,使系统能够在存在不确定性和干扰的情况下保持稳定性和性能。
鲁棒自适应控制的主要优点是能够在存在不确定性和干扰的情况下保持系统的稳定性和性能。
相比于模型参考自适应控制,鲁棒自适应控制对系统模型的要求相对较低,具有更好的适用性和实用性。
然而,鲁棒自适应控制也存在一些挑战,比如对控制器设计的要求较高、控制参数调整的收敛性等。
三、比较分析模型参考自适应控制和鲁棒自适应控制作为两种常见的自适应控制方法,各有优势和劣势。
模型参考自适应控制在鲁棒性和跟踪性能方面具有一定的优势,适用于对系统模型较为精确的情况。
无人驾驶英语PPT
Motion Planning
It determines the specific actions, including acceleration, braking, and steering, that the vehicle needs to take along the planned path
Global Path Planning
This technique plans a complete route for the vehicle from the start to the destination, considering all possible traffic scenarios and objectives
要点二
Semantic Segmentation
It allows the vehicle to understand the scene in detail by assigning semantic means to different parts of the environment
要点三
3D Reconstruction
Level 4
High automation The vehicle can handle most or all driving tasks without human intervention, but limited to specific geographic regions and weather conditions.
Public transportation
Autonomous vehicles can be used for shared rides, shuttle services, or even fully automated bus systems, providing effective and sustainable transportation options for urban areas
robust
JUNE 2014 « IEEE CONTROL SYSTEMS MAGAZINE 105(Registration cochair), Sandra Hirche (Student Activities chair), Pradeep Misra (Electronic Services coordina-tor), Felice Andrea Pellegrino (Public-ity chair), Ann Rundell (Registration cochair), and Andrea Serrani (Publi-cation chair). We also wish to express our sincere gratitude to the sponsors for their generous support. L ast, but not least, we would like to thank Ros-sella Spangaro and “The Office” team for their superb management of the financial and logistic details of the conference and the many students and volunteers of the Universities of Benevento, Firenze, and Trieste for their enthusiastic support during the event.CONCLudING REMARkSQuoting Jay Farrell’s concluding remarks in his CDC 2012 report, “serv-ing as general chair of an IEEE CDC is a once-in-a-lifetime responsibil-ity and opportunity,” and we cannot agree more. This personal journey for us started in an airport lounge on the way back to Italy after the 2008 CDC. During these five years of service, we always felt the support and trust of the CSS as a professional society and of many colleagues and friends. Nodoubt that all our efforts have been motivated by trying to make the first CDC in Italy a memorable event.It is impossible to relate in these short remarks all our memories about this fantastic adventure. So we want to share with the readership two pic-tures (Figure 2). The first was taken on Friday, December 6. We sat at the registration desk exhausted in front of the empty conference center. Look-ing at our faces, it is evident that we could not believe some 1600 attend-ees could be accommodated with the high technical and social standards of our flagship conference. Our faces in the second picture show that, by themiddle of the conference, we started relaxing because the atmosphere was great, the sessions were well attended, and our colleagues and friends enjoyed one of the most beautiful cit-ies in the world. This is an unforget-table reward for us. We are looking forward to the 2014 CDC in Los Ange-les to share some good memories of this fantastic experience that touched our lives.Thomas Parisini andRoberto Tempo General Cochairs,CDC 2013(a)(b)FIGURE 2 (a) Thomas Parisini, Roberto Tempo, Rossella Spangaro, Laura Appiani, and Veronica Simeone before the conference started. (b) Thomas Parisini and Roberto Tem-po during one of the social events.The 25th Chinese Control and decision ConferenceDigital Object Identifier 10.1109/MCS.2014.2308731Date of publication: 12 May 2014The 25th Chinese Control and De-cision Conference (2013 CCDC) was held in Guiyang, China, May 25–27, 2013. The 2013 CCDC was co-organized by Northeastern University, China, the Industrial Electronics (IE) Chapter of the IEEE Singapore Sec-tion, and Harbin Section Chapter of the IEEE Control Systems Society (CSS). The local organizer was Guizhou Uni-versity, China. The conference was technically cosponsored by the CSS, the Systems Engineering Society of China, the Chinese Association for Ar-tificial Intelligence, and the Technical Committee on Control Theory of Chi-nese Association of Automation.TECHNICAL PROGRAMThe 2013 CCDC received 1513 full paper submissions from authors from 26 countries and regions. After going through a rigorous review process—during which all the members in the Technical Program Committee worked professionally, responsibly, and dili-gently—998 papers were included in the technical program for presentation in 99 oral sessions and nine interactive sessions. The conference was attended by 650 delegates.In addition to the regular technical sessions, the technical program also included five keynote addresses and seven distinguished lectures, covering the state of the art in both theory and106 IEEE CONTROL SYSTEMS MAGAZINE » JUNE 2014applications in control, decision, auto-mation, robotics, and emerging technol-ogies. The five keynote addresses were»“Perception-Enabled Control—A New Paradigm for Biomimet-ics and Machine Autonomy” by John Baillieul, Boston Univer-sity, United States»“Recursive Approach to System Identification” by Han-Fu Chen, Chinese Academy of Sciences, China»“Sc ience, Tec h nolog y a nd Industry, Quo Vadis?” by Okyay Kaynak, Bogazici University, Turkey»“Signal Processing via Sampled-data Control—Beyond Shan-non” by Yutaka Yamamoto, Kyoto University, Japan»“Hardware-in-Loop Simula-tion” by Zicai Wang, Harbin Institute of Technology, China.Distinguished lectures were delivered by Xiaohua Xia of the University of Pretoria, South Africa, “Applications of Model Predictive Control in Opera-tion Efficiency Optimization,” Ji-Feng Zhang of the Chinese Academy of Sciences, China, “How Much Infor-mation Is Needed for a Given Control Task,” Jagannathan Sarangapani of the Missouri University of Science and Technology, United States, “Optimal Adaptive Control of Uncertain Non-linear Dynamic Systems,” Jun Wang of The Chinese University of Hong Kong, China, “Neurodynamic OptimizationApproaches to Robust Pole Assign-ment,” Jie Chen of City University of Hong Kong, China, “When Is a Time-Delay System Stable and Stabilizable?,” Guoxiang Gu of Jiangnan University, China, “New Perspectives of Consen-sus Control for Multi-agent Systems,” and Jiafu Tang of Northeastern Univer-sity, China, “Weight Vehicle Routing Problems and Beam Search Combined Max-Min Ant System.”All keynote addresses, distin-guished lectures, oral sessions, and interactive sessions were well attended and produced active discussions. The conference banquet, which included entertainment, was held in the evening of May 26, 2013.A CD-ROM that contained all papers presented at the conference was provided to each registered del-egate. The official conference proceed-ings were published by IEEE and are included in IEEE Xplore .ZHANG SI-YING (CCdC)OuTSTANdING YOuTH AWARdThe Zhang Si-Ying (CCDC) Outstand-ing Youth Paper Award is in recogni-tion of Zhang Si-Ying’s highly regarded perseverance, character, and academic contribution. The award also aims to inspire, motivate, and encourage young scholars in their research. For apaper to be eligible for the award, the first author of the paper must not be older than 35 on the day of the award presentation at the conference. The award is 3000 RMB with a certificate.The Award Committee for the 2013 CCDC was composed of five members:Zhong-Ping Jiang, United States, John Baillieul, United States, Changyun Wen, Singapore, Xin He Xu, China, and Guang-Hong Yang, China.For the 2013 CCDC, 133 papers were selected for consideration for theZhang Si-Ying Outstanding Youth PaperJohn Baillieul delivering a keynote address.Han-Fu Chen delivering a keynote address.Opening ceremony of the 2013 CCDC.Okyay Kaynak delivering a keynote address.Yutaka Yamamoto during his keynote session.JUNE 2014 « IEEE CONTROL SYSTEMS MAGAZINE 107Award based on reviewers’ comments,nominations, and the evaluations of theTechnical Program Committee mem-bers. These papers were sent to famousexperts including some members of theInternational Advisory Committee forfurther evaluation. Based on their evalu-ations and recommendation, the Techni-cal Program Committee short-listed fivepapers as the finalists for the Award:»“Exponential Stability of a Cou-pled Heat-ODE System” by Dong-Xia Zhao and Jun-Min Wang, Beijing Institute of Technology»“Global Exponential Stability of Nonlinear Impulsive Discrete System with Time Delay” by Kexue Zhang and Xinzhi L iu, University of Waterloo»“Nonlinear Oscillations in TCPnetworks with Drop-Tail Buffers” by Nizar Malangadan, Haseen Rahman, and Gaurav Raina, Indian Institute of Technology»“Globally Adaptive Path Track-ing Control of Under-actuated Ships” by Wei Wang and Jiangsh-uai Huang, Tsinghua University »“Finite Time Containment Con-trol of Nonlinear Multiagent Networks” by Di Yu, Lijuan Bai, and Weijian Ren, Northeast Petroleum University.During the conference, all Award Committee members attended the oral presentations of the five finalist papers. Each member independently assessedtheir originality, technical quality, and written and oral presentations. On the basis of these assessments, the 2013 CCDC Zhang Si-Ying Outstanding Youth Paper Award was presented toWei Wang from Tsinghua University.The International Advisory Commit-tee Chair of 2013 CCDC, Si-Ying Zhang, presented certificates to all the finalists and presented the award with a certifi-cate to the winner during the banquet.THE 26TH CHINESE CONTROLANd dECISION CONFERENCEThe 26th CCDC (2014 CCDC) will be held in Changsha, China, May 31 to June 2, 2014. Keynote addresses will be deliv-ered by Alessandro Astolfi of Imperial College, United Kingdom, Daizhan Cheng of the Chinese Academy of Sci-ences, and Jay A. Farrell of University of California, Riverside, United States. The Organizing Committee will invite prominent researchers as well as dis-tinguished lecturers. Information about the 2014 CCDC will be consistently updated on the conference Web site: .Changyun Wen andZhong-Ping Jiang International TechnicalProgram ChairsA well-attended banquet.2013 CCDC award committee members and finalists of the Zhang Si-Ying Outstanding Youth Award.Ke Fang, who delivered the keynote ad-dress for Prof. Zicai Wang.。
对人工智能是好是坏的高三英语作文
对人工智能是好是坏的高三英语作文Artificial Intelligence: A Double-Edged SwordThe advent of artificial intelligence (AI) has ushered in a new era of technological advancement, transforming the way we live, work, and interact with the world around us. As we delve deeper into the realm of AI, we are confronted with a complex and multifaceted debate – is AI a blessing or a curse for humanity? This question has become the subject of intense discussion and deliberation, with proponents and critics alike presenting compelling arguments.On the one hand, the potential benefits of AI are undeniable. AI-powered systems have revolutionized industries, streamlining processes, enhancing efficiency, and unlocking new frontiers of innovation. In the medical field, AI-driven diagnostics and drug discovery have the power to save lives, while in the financial sector, AI algorithms can detect fraud and optimize investment strategies. In transportation, autonomous vehicles promise to reduce accidents and traffic congestion, while in education, AI-powered adaptive learning platforms can personalize the learning experience for each student.Moreover, AI has the potential to tackle some of the world's most pressing challenges, such as climate change, food scarcity, and global health crises. Through predictive analytics, AI-powered systems can help us anticipate and mitigate the effects of natural disasters, while AI-driven advancements in renewable energy and sustainable agriculture can contribute to a more eco-friendly future.However, the dark side of AI cannot be ignored. As AI systems become more sophisticated and ubiquitous, concerns about their impact on employment and job security have risen to the forefront. Automation and AI-driven automation have the potential to displace millions of workers, particularly in industries that rely on repetitive, routine tasks. This displacement could lead to widespread unemployment, social unrest, and the exacerbation of existing economic inequalities.Furthermore, the ethical implications of AI are a source of growing concern. As AI systems become more autonomous and make decisions that affect human lives, questions arise about accountability, bias, and the preservation of human agency. AI algorithms can perpetuate and amplify societal biases, leading to discriminatory outcomes in areas such as hiring, lending, and criminal justice. Additionally, the increasing use of AI in surveillance and decision-making processes raises privacy concerns and challenges the traditional notions of individual rights and freedoms.Another significant concern is the potential for AI to be weaponized and used for malicious purposes. Autonomous weapons systems, cyber attacks, and the spread of misinformation through AI-generated content pose serious threats to global security and stability. The development of AI-powered surveillance and manipulation tools can also enable authoritarian regimes to tighten their grip on power and suppress dissent.The rapid advancements in AI also raise existential questions about the long-term impact on humanity. As AI systems become more intelligent and capable, the possibility of a technological singularity –a point where AI surpasses human intelligence and becomes self-improving – looms large. This scenario, often referred to as the "AI apocalypse," has sparked fears about the potential for AI to spiral out of control and pose an existential threat to humanity.In light of these multifaceted concerns, it is clear that the debate surrounding AI is far from simple. The future of AI will depend on our ability to navigate the complex ethical, social, and political implications of this technology. Policymakers, researchers, and the public must work together to develop robust frameworks for the responsible development and deployment of AI, ensuring that the benefits of this technology are equitably distributed and the risks are mitigated.Ultimately, the impact of AI on humanity will be determined by the choices we make today. By fostering a nuanced understanding of the challenges and opportunities presented by AI, we can harness its potential to improve lives while safeguarding against its misuse. The path forward requires a delicate balance between embracing the transformative power of AI and maintaining our commitment to human values, rights, and well-being.。
53生产全流程多目标动态优化决策与控制一体化理论及应用
(National RFID Centre) Lee Eng Wah教授提出该项目组“提出了一 种改进的差分进化算法,采用嵌入增量机制和实时事件出现时前一差分 进化求解过程的最终种群生成新的初始种群”。原文:"They proposed an improved differential evolution(DE) algorithm by embedding an incremental mechanism to generate a new initial population for the DE whenever a real-time event arises, based on the final population in the last DE solution process代表性论文[2]入选了ESI高被 引论文。
(2)第三方对科学发现2的评价[代表性论文1,2,3]
引文[3]引用了代表性论文[3]。澳大利亚New South Wales大学Ruhul Sarker教授指出该项目组提出的智能优化算法“对28个无约朿 问题算例进行了测试,结果表明它的性能优于最先进的算法”。原文:“Th亡algorithm was tested on 28 unconstrained problems, with the results demonstrated that it was superior to state-of-the-art algorithms"o代表性论文[3]入选了ESI高被引论文。
2019
项目名称
生产全流程多目标动态优化决策与控制一体化理论及应用提源自单位教育部提名意见
(不超过600字)
基于数据驱动控制的鱼雷侧向运动控制研究
第43卷第2期2021年4月指挥控制与仿真CommandControl&SimulationVol 43㊀No 2Apr 2021文章编号:1673⁃3819(2021)02⁃0061⁃04基于数据驱动控制的鱼雷侧向运动控制研究李㊀恒,曹㊀渊,陈㊀轶,赵㊀江(海军研究院,北京㊀102442)摘㊀要:鱼雷作为水下作战的重要武器,其控制技术一直是国内外水下武器领域研究的热点问题㊂采用了基于数据驱动控制的控制方法,研究了鱼雷的侧向运动控制㊂当雷体的流体动力学参数未知时,仅利用雷体的输入输出数据,设计直舵角的控制律㊂这种方法在工程上计算量少,易于实现,因此实用性较强㊂在设计基于数据驱动控制的鱼雷垂直舵角控制律的过程中,通过雷体的输入输出数据,估计鱼雷的未知动态模型以及离散后的误差模型,设计参数更新律来估计未知参数㊂再结合滑模控制技术,得到了最终的控制算法㊂通过李雅普诺夫方法,证明了所设计控制律的稳定性㊂最后的仿真实验也说明了所提算法的有效性㊂关键词:数据驱动控制;滑模控制;鱼雷;侧向运动中图分类号:E925 23㊀㊀㊀㊀文献标志码:A㊀㊀㊀㊀DOI:10.3969/j.issn.1673⁃3819.2021.02.011LateralMotionDynamicsControlofTorpedoBasedonData⁃drivenControlLIHeng,CAOYuan,CHENYi,ZHAOJiang(NavalResearchAcademy,Beijing102442,China)Abstract:Asakeyweaponofunderwatermilitarybattle,thecontroltechnologyoftorpedohasbeenthehotissueallovertheworld.Adata⁃drivencontrolbasedmethodisproposedfortheresearchoflateraldynamicscontroloftorpedo.Whenthehydrodynamicparametersoftorpedoareunknown,theinput⁃outputdataisusedtodesignthecontrolschemeoftheverticalrudderangel.Becausethedata⁃drivenbasedcontrolmethodneedslittlecalculation,itismorepracticaltorealizeintherealsystems.Inthedesigningprocessofdata⁃drivenbasedcontrolalgorithmofverticalrudderangel,theunknowndynamicsandtheerrorofthediscretizationareestimatedbyspecialparameter⁃identifiedupdatedlawthroughtheinput⁃outputdatainthispaper.Combinedwiththeslidingmodecontroltechnology,thedata⁃drivenbasedalgorithmoftheverticalrudderangelcomesout.ItisprovedtobestableusingLyapunovstabilitymethod.Thesimulationresultsalsoindicatetheeffectivenessofthepro⁃posedalgorithm.Keywords:data⁃drivencontrol;slidingmodecontrol;torpedo;lateralmotiondynamics收稿日期:2020⁃12⁃21修回日期:2020⁃12⁃29作者简介:李㊀恒(1982 ),男,湖北武汉人,博士,高级工程师,研究方向为鱼雷武器系统㊂曹㊀渊(1985 ),男,博士,工程师㊂㊀㊀鱼雷作为水下作战最有效的武器,其控制技术一直是国内外水下武器领域研究的热点问题㊂随着现代科技迅速发展,鱼雷正朝着航行深度深,航行范围大,航行路程远,航行速度高,雷体特征参数变化范围大等方向发展㊂因此,传统的PID控制技术已经不能满足鱼雷的控制需求,自适应控制㊁滑模控制㊁反步控制㊁最优控制等现代控制理论技术正逐步运用于鱼雷的控制上面㊂文献[1]采用了滑模变结构控制方法,设计了鱼雷侧向运动的控制律㊂文献[2]采用滑模模糊控制方法,解决了无法有效抑制鱼雷横滚的问题㊂文献[3]设计了基于变结构控制的反鱼雷(ATT)导引律,导引律中取变结构控制器使得ATT与来袭鱼雷间视线角保持恒定,且控制器对模型参数摄动具有强鲁棒性㊂文献[4]采用高阶滑模控制器,解决了鱼雷控制过程中抖颤的问题㊂数据驱动控制是指受控系统控制器的设计不包含受控过程数学模型信息,仅利用受控系统的在线和离线输入输出数据以及经过处理而得到的信息来设计系统控制算法,并且在一定的假设下,控制器使系统具有收敛性㊁稳定性以及鲁棒性[5]㊂从数据驱动控制的定义可以看出,发展数据驱动控制理论与方法是新时期控制理论发展与重大应用的必然要求,具有重要的理论与现实意义,因此数据驱动控制理论的研究受到了越来越多中外学者的关注㊂文献[6]研究了在有预设暂态约束条件下的数据驱动控制方法㊂文献[7]研究了基于无模型自适应控制方法的水面无人船侧滑角控制㊂文献[8]研究了基于无模型自适应迭代控制的多异构非线性智能体的编队控制问题㊂文献[9]设计了一种基于实时数据驱动的无模型自适应控制方法,实现了船舶的稳定操控㊂鱼雷的动力学模型具有非线性㊁强耦合㊁流体动力参数众多等特点,因此通常把鱼雷的空间运动分解为纵向运动和侧向运动㊂这样的分解,是一种简化方法,把一个复杂系统分解为较简单的几个子系统,暂时略去子系统之间的交连耦合作用,以便于问题的研62㊀李㊀恒,等:基于数据驱动控制的鱼雷侧向运动控制研究第43卷究[10]㊂鱼雷的侧向运动是指鱼雷在水平面(地面坐标系平面)内的运动㊂鱼雷的侧向运动包括鱼雷在水平面内的运动和绕轴的转动㊂本文针对鱼雷流体动力参数未知的情况,采用基于数据驱动控制的滑模变结构方法,设计了基于数据驱动控制的垂直舵角算法,控制鱼雷的侧向运动,并通过李雅普诺夫稳定性方法以及数学仿真来说明所提算法的有效性㊂1㊀鱼雷模型1 1㊀鱼雷侧向运动方程组本文仅考虑无横滚运动下的鱼雷侧向运动,鱼雷的侧向运动方程组如下mv0̇Ψ=-12ρv20SLmβzβ+mδzδr+m ωz ωy()+λ33̇β+λ35ω㊃-y+Tβ(Jyy+λ55)̇ωy=12ρv20SLmβyβ+mδyδr+m ωy ωy()-λ35̇β̇ψ=ωẏz0=-v0sinΨΨ=ψ-βìîíïïïïïïïïïïïïïï(1)其中,Ψ为弹道偏角,ωy为偏航角速度,zo为侧向位移,ψ为偏航角,δr为鱼雷直舵角㊂1 2㊀鱼雷侧向运动模型离散化由式(1)可得如下方程组̇ψ=ωẏωy=fβ,̇β, ωy()+gδr{(2)其中,fβ,̇β, ωy()=12ρv20SLmβyβ+m ωy ωy()-λ35̇βéëêêùûúúJyy+λ55,g=12ρv20SLmδy/(Jyy+λ55)将式(2)离散化,可得如下方程组:ψ(k+1)=ψ(k)+Tsωy(k)ωy(k+1)=ωy(k)+ξ(k)δr(k){(3)其中,ξ(k)=Ts[f(k)δr(k)+g+Ω(k)]表示系统的未知动态,Ω(k)是侧向运动方程离散化后系统未知的未建模部分,Ts为采样时间㊂2㊀控制律设计2 1㊀数据驱动控制对于如下的离散非线性系统:y(k+1)=f(y(k), ,y(k-ny),u(k), ,u(k-nu))(4)其中,u(k)㊁y(k)分别为系统在第k时刻的输入与输出,ny㊁nu分别为两个未知的正整数,f( )表示系统未知的模型㊂基于上述离散系统,有以下假设[11]:假设1)离散系统的输入㊁输出均可控可观测㊂假设2)未知函数f( )对于系统任意时刻输入或者输出的偏导数存在㊂假设3)非线性系统(4)满足Lipschitz条件,即f(y(k),u(k))-fyk-1(),uk-1()()ɤbu(k)-uk-1()其中,b是一个正常数㊂满足上述假设的系统,其模型可以表示成如下紧格式动态线性化模型:y(k+1)=y(k)+ϕΔu(k)(5)其中,ϕ为系统的伪偏导数㊂系统(5)很容易设计控制输入对系统进行控制,它将一个非线性系统等价转化为带有一个时变标量参数的线性时变系统㊂利用最小化加权一步向前预报误差准则函数,可得如下控制输入设计方案:u(k)=uk-1()+ρkϕ(k)λ+^ϕ(k)2yd(k+1)-y(k)()ϕ(k)=ϕk-1()+ηkΔuk-1()λ+Δuk-1()2∗y(k)-^ϕ(k-1)Δuk-1()()(6)从上述控制算法和参数更新律可以看出,式(5)㊁式(6)的设计与系统的数学模型以及模型参数无关,仅利用测量到的输入输出数据进行控制输入的设计,该控制算法可以实现受控系统的参数自适应控制㊂由离散后的鱼雷侧向运动方程组可知,式(3)满足假设1)㊁2)㊁3)的所有条件,因此可以采用数据驱动控制方法来控制鱼雷的侧向运动㊂2 2㊀鱼雷垂直舵角的控制律设计对于式(3),令^ξ表示为系统未知动态ξ的估计值,则关于^ξ的预设误差准则函数可以设计成如下形式:J(^ξ)=ωy(k+1)-ωy(k)-^ξ(k)δr(k)2+μ^ξ(k+1)-^ξ(k)2(7)令∂J(^ξ(k))∂^ξ(k)=0,可以得到:^ξ(k+1)=^ξ(k)+pδr(k)μ+δ2r(k)(Δωy(k+1)-^ξ(k)δr(k)^ξ(k+1)=^ξ(1),当^ξ(k+1)<ε或sgn(^ξ(k))ʂsgn(^ξ(1))(8)第2期指挥控制与仿真63㊀式中,pɪ(0,1)是一个常数,ε是一个任意小的常数,sgn(㊀)表示符号函数㊂考虑滑模面s(k+1)=e(k+1)+Ce(k)其中,e(k+1)=ωy(k+1)-ωd(k),系数Cɪ(0,1)是正常数,ωd(k)表示期望的角速度㊂令滑模面s(k+1)=0,结合式(7)㊁(8)得到直舵角控制律为δr(k)=ω(d(k)-ωy(k)-Ce(k)-ρsgn(s))/(^ξ(k)+σ)(9)其中,σ为正常数,算法流程如表1所示㊂表1㊀垂直舵角算法设计流程示意算法㊀基于数据驱动控制的鱼雷垂直舵角控制律设计输入:ψd(k)㊁ωd(k)㊁ψ(k)㊁ωy(k)㊁δr(k)㊁^ξ(k)输出:ωy(k+1)㊁ψ(k+1)㊁^ξ(k+1)1.㊀初始化参数2.㊀fork=0,k<K,k++3.㊀e(k+1)ѳωy(k+1)-ωd(k)4.㊀s(k+1)ѳe(k+1)+Ce(k)5.㊀^ξ(k+1)ѳ^ξ(k)+pδr(k)μ+δ2r(k)Δωy(k+1)-^ξ(k)δr(k)()6.㊀δr(k+1)ѳ(ωd(k)-ωy(k)-Ce(k)-ρsgn(s))/(^ξ(k)+σ)7.㊀endforstopcommand㊀2 3㊀稳定性证明式(3)可改写为ωy(k+1)=ωy(k)+^ξ(k)+σ()δr(k)+υ(k)式中,υ(k)=ξ(k)-^ξ(k)-σ()δr(k)㊂考虑如下李雅普诺夫方程V(k)=|s(k)|(10)考虑式(8)与(10),则可以得到V(k+1)-V(k)=ΔV(k)由上分析,可以推断出υ(k)是有界的,那么存在一个正常数b,使得maxiɪ{2, ,k}υ(i)-υ(i-1)<b成立,那么当ΔV(k)<0时,s(k)>b+ρ㊂最终,s(k)会收敛到以0为邻域的区域内,因此可以说明e(k)的有界性㊂3㊀仿真实验分析考虑如下的鱼雷侧向运动方程组:̇ψ=ωẏωy=0 08v20(0 8β-0 2δr-0 6ωy)仿真时间设为20s,仿真步长Ts=0 01s,雷体速度取v0=13m/s,ψ㊁ωy初始值为0,ωd=0,ψd=10/57 3,^ξ(0)=0,仿真结果如图1㊁2所示㊂图1㊀鱼雷偏航角变化曲线图2㊀鱼雷偏航角速率变化曲线上述仿真中,取参数p=0 5,ρ=5 4,C=0 8,σ=0 01㊂从上述仿真结果可以看出,利用本文所提控制律对鱼雷的偏航角进行控制,可以使偏航角快速趋近于期望角度,没有超调产生,并且偏航角速率也控制在期望范围内,因此控制算法达到了期望的效果㊂4㊀结束语本文采用了基于数据驱动控制的控制方法,研究了鱼雷的侧向运动控制㊂得到雷体的侧向运动方程组以及其离散化形式之后,由于参数未知时,仅利用雷体的输入输出数据,设计直舵角的控制律㊂通过雷体的输入输出信息,估计鱼雷的未知动态模型以及离散后的模型误差㊂再结合滑模控制技术,设计了雷体直舵角的控制律㊂通过李雅普诺夫方法,证明了所设计控制律的稳定性㊂最后的仿真实验也说明了所提算法的有效性㊂参考文献:[1]㊀胡蔷,高立娥,刘卫东.鱼雷侧向运动的滑模变结构64㊀李㊀恒,等:基于数据驱动控制的鱼雷侧向运动控制研究第43卷控制仿真研究[C]ʊ中国西部声学学术交流会论文集,2015.[2]㊀王珲,潘雷.基于滑模模糊控制的鱼雷横滚控制方法研究[J].舰船电子工程,2013,33(5):150⁃152.[3]㊀李宗吉,张西勇,练永庆.基于变结构控制的反鱼雷鱼雷导引律鲁棒性研究[J].鱼雷技术,2014,22(4):272⁃276.[4]㊀RhifA.AHighOrderSlidingModeControlwithPIDSlid⁃ingSurface:SimulationonaTorpedo[J].InternationalJournalofInformationTechnology,ControlandAutomation,2012,2(1):1⁃13.[5]㊀侯忠生,许建新.数据驱动控制理论及方法的回顾和展望[J].自动化学报,2009,35(6):650⁃667.[6]㊀ZhangW,XuD,JiangB,etal.PrescribedPerformancebasedModel⁃FreeAdaptiveSlidingModeConstrainedControlforaClassofNonlinearSystems[J].InformationSciences,2020(544):97⁃116.[7]㊀WengY,WangN,SoaresCG.Data⁃DrivenSideslipOb⁃serverbasedAdaptiveSliding⁃ModePath⁃FollowingControlofUnderactuatedMarineVessels[J].OceanEngi⁃neering,2020(197):106910.[8]㊀RenY,HouZ.RobustModel⁃freeAdaptiveIterativeLearningFormationforUnknownHeterogeneousNonlinearMulti⁃AgentSystems[J].IETControlTheory&Applica⁃tions,2019,14(4):654⁃663.[9]㊀熊勇,余嘉俊,牟军敏,等.基于数据驱动控制的船舶自动靠泊[J].中国航海,2020,43(3):1⁃7.[10]严卫生.鱼雷航行力学[M].西安:西北工业大学出版社,2005:248⁃252.[11]HouZ,ChiR,GaoH.AnOverviewofDynamicLinear⁃ization⁃BasedData⁃DrivenControlandApplications[J].IEEETransactionsonIndustrialElectronics,2016,64(5):4076⁃4090.(责任编辑:许韦韦)。
雅思英文议论文 职业选择与规划主题
Career Choice and Planning: Navigating the Path to SuccessIntroductionCareer choice and planning represent critical decisions in an individual's life, shaping their future trajectory. In today's rapidly evolving world, characterized by diverse opportunities and challenges, making informed career decisions and establishing a well-structured plan are indispensable. This comprehensive essay delves into the paramount importance of career choice and planning, elucidates the multifaceted factors that influence these decisions, and provides an in-depth exploration of effective strategies to successfully navigate the labyrinthine path to a fulfilling and prosperous career.The Significance of Career Choice and PlanningCareer choice and planning extend beyond mere occupation selection; they encompass the crafting of a meaningful and purposeful life. The significance of these processes can be summarized through several compelling reasons:1.Personal Fulfillment: A judiciously chosen careerresonates with an individual's passions, interests, and values, leading to unparalleled personal satisfaction and profound job contentment.2.Economic Stability: Meticulously planned careers areoften synonymous with financial stability and security, granting individuals the capacity to fulfill both their needs and aspirations.3.Professional Growth: Strategic career planning facilitatescontinuous learning and development, thereby enhancing job prospects and increasing earning potential over time.4.Contribution to Society: Well-chosen careers provideopportunities for individuals to contribute meaningfully to society, whether through advancements in healthcare, innovations, education, or other avenues.5.Life Balance: Career planning affords individuals theability to strike a harmonious balance between professional commitments, familial responsibilities, and personal pursuits, ultimately fostering holistic well-being.Influential Factors in Career ChoicesNumerous factors wield considerable influence over an individual's career choices and planning:1.Interests and Passions: Personal predilections and fervoroften steer career choices, as individuals tend to excel in vocations they ardently embrace.2.Skills and Abilities: A candid appraisal of an individual's innate talents and competencies is instrumental in pinpointing suitable career options.cational Attainment and Training: Academic qualifications and specialized training play a pivotal role in determining career avenues, as distinct professions necessitate varying levels of education and expertise.4.Economic Considerations: Financial stability and earning prospects wield substantial sway over career choices, with individuals often favoring vocations offering competitive salaries and job security.5.Family and Societal Expectations: Cultural norms and familial expectations can exert significant pressure on career decisions, occasionally steering individuals toward certain professions to fulfill societal or familial mandates.6.Market Dynamics: Job availability and market trends heavily influence career choices, as some industries burgeon with opportunities while others grapple with scarcity.7.Alignment with Personal Values: Ethical and moral values frequently factor into career choices, as individuals are often drawn to professions that harmonize with their core principles and values.Strategies for Effective Career PlanningStrategically structured career planning is an intricate process that necessitates meticulous consideration and deliberate actions:1.Self-Assessment: The inception of effective careerplanning commences with rigorous self-assessment. Delve into your interests, strengths, weaknesses, and values, as a profound understanding of oneself is the bedrock of astute career choices.2.Thorough Research: Extensive research on diversecareers and industries is paramount to comprehend job requisites, growth trajectories, and prevailing market dynamics.3.Goal Setting: Prudent career planning hinges on settingtangible, achievable career objectives. These goals provide a navigational compass, guiding career choices and actions.cational Pursuits and Skill Enhancement: To qualifyfor your chosen career path, diligently pursue the requisite educational credentials and skill development. This might encompass degrees, certifications, or specialized training programs.work Building: Cultivating a robust professionalnetwork by engaging with mentors, peers, and industry experts can open doors to invaluable career opportunities and guidance.6.Flexibility and Adaptability: In an ever-changing joblandscape, an adaptive mindset is indispensable. Being open tonovel career opportunities and willing to confront challenges with resilience are key traits of successful career planning.7.Seek Expert Counsel: Consultation with careercounselors or seasoned mentors can provide invaluable insights and guidance throughout your career planning journey.8.Lifelong Learning: Make a steadfast commitment tolifelong learning to remain competitive and pertinent in your chosen field, given the continuous evolution of industries and job requirements.Challenges Encountered in Career Choice and PlanningWhile career choice and planning are of paramount importance, they are not devoid of challenges:1.Uncertainty: The fluidity of the job market, coupled withthe unpredictability of future trends, can instigate profound uncertainty in career planning.2.External Pressures: External pressures, emanating fromfamilial, societal, or peer influences, may exert undue sway over an individual's career decisions, potentially steering them away from their true vocational calling.3.Intense Competition: Many career domains arecharacterized by fierce competition, necessitating continuous skill enhancement and the cultivation of unique attributes to stand out.4.Work-Life Balance: Striking an equilibrium betweenprofessional commitments and personal life pursuits can pose a formidable challenge, particularly in demanding careers.ConclusionIn summary, career choice and planning are transformative processes that meticulously mold an individual's life journey. Making informed decisions steeped in personal interests, values, and aspirations is indispensable for cultivating career contentment and achieving success. By comprehending the myriad factors influencing career choices and conscientiously implementing effective career planning strategies, individuals can confidently navigate the intricate labyrinth of the professional sphere, endeavoring to realize their aspirations.In a dynamic and ever-evolving job market, career choice and planning continue to serve as indispensable tools for forging a meaningful and prosperous future. The journey towards a gratifying career may be replete with challenges, but with unwavering dedication, adaptability, and a crystalline vision, individuals can chart their course to success. In doing so, they not only enhance their personal well-being but also contribute significantly to the betterment of society at large. Indeed, career choice and planning are the compass guiding individuals towards a brighter future where personal ambitions merge harmoniously with professional accomplishments.。
ai 英语作文
ai 英语作文Title: The Impact of Artificial Intelligence on Education。
Introduction:Artificial Intelligence (AI) has been makingsignificant strides in various fields, and education is no exception. In recent years, AI technologies have been increasingly integrated into educational settings, revolutionizing the way students learn and teachers teach. This essay explores the impact of AI on education, focusing on its benefits, challenges, and future prospects.Benefits of AI in Education:One of the primary benefits of AI in education is its ability to personalize learning experiences. AI-powered adaptive learning platforms can analyze students' strengths, weaknesses, and learning styles to deliver customizedcontent and recommendations. This personalized approach helps students learn at their own pace and grasp concepts more effectively.Moreover, AI can enhance student engagement through interactive learning experiences. Virtual tutors and chatbots equipped with natural language processing capabilities can provide instant feedback and support to students, making learning more interactive and engaging. Additionally, AI-driven educational games and simulations can make learning fun and immersive, motivating students to actively participate in the learning process.Furthermore, AI can assist teachers in administrative tasks, such as grading assignments and managing classroom resources. Automated grading systems can save teachers valuable time, allowing them to focus more on lesson planning and student interaction. AI-powered analyticstools can also help educators track student progress and identify areas that need additional attention, enabling targeted interventions and support.Challenges and Considerations:Despite its numerous benefits, the integration of AI in education also poses certain challenges and considerations. One concern is the potential for widening the digital divide. Not all students have equal access to technology and resources, which can exacerbate existing inequalitiesin education. Therefore, efforts must be made to ensure equitable access to AI-powered educational tools and resources for all students, regardless of their socioeconomic background.Another challenge is the ethical implications of AI in education, particularly regarding data privacy and algorithmic bias. AI systems collect vast amounts of data on students' learning behaviors and performance, raising concerns about data privacy and security. Moreover, algorithmic bias in AI algorithms can perpetuate existing inequalities and stereotypes, potentially disadvantaging certain groups of students. It is essential to address these ethical concerns through robust data protection regulations and algorithmic transparency measures.Future Prospects:Looking ahead, the future of AI in education holds immense potential for innovation and transformation. As AI technologies continue to evolve, we can expect to see advancements in personalized learning, adaptive assessment, and intelligent tutoring systems. Virtual reality (VR) and augmented reality (AR) technologies powered by AI could create immersive learning environments that simulate real-world scenarios and enhance experiential learning.Furthermore, AI could play a crucial role in addressing global challenges in education, such as teacher shortages and quality education delivery in remote areas. AI-powered educational platforms and chatbots could supplement traditional teaching methods, providing accessible and affordable learning opportunities to students worldwide.Conclusion:In conclusion, AI has the power to revolutionizeeducation by personalizing learning experiences, enhancing student engagement, and assisting teachers inadministrative tasks. However, its integration into education must be approached thoughtfully, taking into account considerations such as equity, ethics, and privacy. By harnessing the potential of AI responsibly, we can create a more inclusive, interactive, and effective learning environment for students worldwide.。
基于单片机的机械臂运行轨迹在线控制系统设计
基于单片机的机械臂运行轨迹在线控制系统设计宋东亚【摘要】基于PLC的机械臂运行轨迹控制系统通过PLC采集现场信号及输出信号的状态变化实现机械臂运行轨迹的控制,不能实现多自由度机械臂控制.设计基于单片机的机械臂运行轨迹在线控制系统,系统硬件由上位机PC在线控制、主控制板和机械臂舵机控制板构成,通过光电编码器位移传感器实现机械臂位置、位移感觉,利用舵机控制板采用Arduino舵机扩展板和D-H理论,构建机械臂结构模型,实现多自由度机械臂的控制.系统软件主要由上位机在线控制部分、主控制板控制程序和舵机控制板程序组成,由主控板控制程序和上位机在线控制程序两部分实现机械臂控制,通过单片机系统时钟初始化提高系统的运行速度.实验结果表明,所设计的系统能够稳定、快速地实现机械臂轨迹控制,并且准确度高.【期刊名称】《现代电子技术》【年(卷),期】2018(041)018【总页数】4页(P174-177)【关键词】单片机;机械臂;运行轨迹;舵机控制;光电编码器;位移传感器【作者】宋东亚【作者单位】郑州工业应用技术学院,河南新郑 451150【正文语种】中文【中图分类】TN876-34;TP311随着当代社会信息技术和生产自动化程度的突飞猛进,机械人也随之步入高度自动化、智能化的阶段,它替代传统的人工作业方式,减轻劳动量的同时,还可以提高生产效率、降低生产成本,并且使因人工疏忽导致的安全事故得到极大的减少[1],在生产、生活中扮演着越来越重要的角色,已成为现代化生产中至关重要的环节。
在机械人技术领域中,机械臂通过自动控制具有操作功能和移动功能[2],可以通过编程来完成各种作业,广泛的应用在设备装配、自动喷漆、自动化生产线、教育研究等领域。
传统的基于PLC的机械臂运行轨迹控制系统不能实现多自由度控制,并且存在稳定性差以及精度低的缺点。
针对这种情况,本文设计了基于单片机的机械臂运行轨迹在线控制系统。
1 基于单片机的机械臂运行轨迹在线控制系统1.1 系统硬件结构设计系统的硬件主要包括上位机PC在线控制、主控制板和机械臂舵机控制板三部分。
基于LOS导航的欠驱动船舶滑模控制
基于LOS导航的欠驱动船舶滑模控制秦梓荷;林壮;李平;李晓文【摘要】考虑存在外界随机海浪干扰和模型参数不确定的欠驱动船舶非线性运动模型,提出一种基于可视距(LOS)导航的滑模控制器.通过设计切换面克服参数不确定以及波浪扰动对反馈控制带来的困难,并在滑模控制的基础上引入低通滤波器,消除因扰动和滑模切换面自身引起的横漂速度和艏摇速度的高频振荡,实现速度的光滑控制.利用李亚普诺夫理论对控制算法的稳定性进行证明,并进行一艘单体船的数值仿真实验.研究结果表明:所设计的控制器是有效的,且具有良好的鲁棒性.【期刊名称】《中南大学学报(自然科学版)》【年(卷),期】2016(047)010【总页数】7页(P3605-3611)【关键词】欠驱动船舶;路径跟踪;可视距导航;滑模控制;鲁棒性【作者】秦梓荷;林壮;李平;李晓文【作者单位】哈尔滨工程大学船舶工程学院,黑龙江哈尔滨,150001;哈尔滨工程大学船舶工程学院,黑龙江哈尔滨,150001;哈尔滨工程大学船舶工程学院,黑龙江哈尔滨,150001;哈尔滨工程大学船舶工程学院,黑龙江哈尔滨,150001【正文语种】中文【中图分类】TP273对欠驱动船舶的轨迹跟踪和路径跟踪控制而言,主要难点在于只有艏摇和纵向速度是直接驱动的,而横漂速度则无驱动。
考虑到经济性,该种欠驱动配置是目前水面船舶最普遍采用的形式[1]。
为确保欠驱动船舶的航行安全,同时提高自动驾驶控制效率,针对欠驱动船舶的控制算法研究具有突出的实际意义。
在欠驱动系统的控制算法方面,已有相关研究成果[2−4]。
目前常用的欠驱动控制方法有反步法、滑模理论、级联理论、李亚普诺夫理论等。
杨莹等[5]基于反步法和参数自适应的方法,针对未知海流以及模型参数不确定情况下欠驱动AUV的三维路径跟踪进行了研究。
DO等[6]基于级联系统方法和利普西斯投影算法,对欠驱动船舶的路径跟踪问题进行了鲁棒自适应算法研究。
丁磊等[7]为解决欠驱动船队的编队控制问题,结合反步法和领导−跟随法设计了编队控制器,实现了多欠驱动系统的位置误差镇定和航向稳定性控制,但未计及波浪扰动的影响。
数理基础英语
数理基础英语The English language has long been recognized as a global lingua franca, facilitating communication and collaboration across diverse cultures and disciplines. However, the foundations of this linguistic dominance can be traced back to the robust mathematical and scientific principles that underpin the very structure and evolution of the language. In this essay, we will explore the intricate connections between the realms of mathematics, science, and the English language, highlighting how these interdisciplinary relationships have shaped the development and widespread adoption of English worldwide.At the core of the English language lies a remarkable system of logic and patterns. Much like the elegant equations and theorems that govern the natural world, the grammar, syntax, and semantics of English adhere to a set of well-defined rules and principles. These linguistic structures exhibit a remarkable level of consistency and predictability, akin to the universal laws of mathematics and science.One of the most striking examples of this mathematical influence canbe found in the phonetic structure of the English language. The pronunciation of words, governed by a complex system of vowels, consonants, and diphthongs, can be viewed as a form of "linguistic algebra." Just as mathematical equations rely on the precise manipulation of variables and operators, the spoken English language utilizes a carefully orchestrated combination of sound elements to convey meaning. This systematic approach to phonetics not only facilitates clear and efficient communication but also enables the development of advanced language-processing technologies, such as speech recognition and text-to-speech systems.Furthermore, the written form of the English language demonstrates a profound connection to the principles of mathematics and science. The alphabetic script, with its 26 letters, can be seen as a numerical system in its own right, where each letter represents a unique symbol with specific rules for combination and manipulation. This underlying numerical framework allows for the efficient encoding and decoding of information, much like the binary systems that underpin modern digital technologies.Moreover, the evolution of the English language has been heavily influenced by the advancement of scientific knowledge and the need for precise terminology. As new discoveries and innovations have emerged across various fields, the English language has rapidly adapted, incorporating technical vocabulary and specializedterminology to accurately describe and communicate these developments. From the intricate nomenclature of biology and chemistry to the complex mathematical notations used in physics and engineering, the English language has seamlessly integrated these scientific concepts, further reinforcing its status as a versatile and adaptable medium of global communication.Beyond the structural and lexical connections, the very process of learning and mastering the English language can be viewed through the lens of scientific and mathematical principles. The acquisition of language skills, such as reading, writing, and grammar comprehension, often involves the application of cognitive strategies akin to problem-solving and data analysis. Students of English must develop an understanding of the underlying patterns, rules, and relationships that govern the language, much like the way scientists and mathematicians approach the study of the natural world.Moreover, the teaching and assessment of English language proficiency have increasingly incorporated quantitative and data-driven approaches. Standardized tests, such as the TOEFL and IELTS, rely on statistical analysis and psychometric models to evaluate an individual's language abilities, drawing upon the principles of measurement and evaluation that are central to the scientific method.In conclusion, the profound interplay between the English languageand the realms of mathematics and science has been instrumental in shaping the global dominance of English. From its underlying logical structures to its adaptive capacity in incorporating technical terminology, the English language has seamlessly integrated the principles of the scientific and mathematical disciplines. This symbiotic relationship has not only enhanced the effectiveness and efficiency of communication but has also paved the way for the development of advanced language-processing technologies and data-driven approaches to language learning and assessment. As the world continues to grapple with the challenges of globalization and technological advancement, the strong foundations of mathematics and science will undoubtedly continue to play a pivotal role in the ongoing evolution and expansion of the English language.。
英文文献总结
E1.Robust Global Trajectory Tracking for Underactuated VTOL Aerial Vehicles using Inner-Outer Loop Control ParadigmsSOURCE:IEEE TRANSACTIONS ON AUTOMATIC CONTROL现有的姿态-位置双闭环稳定控制的思路是通过独立的调节各环节参数实现系统稳定。
但是这一类方法需要控制对象的先验知识,并且在实际使用过程中并不是那么地有效率,因为外界干扰和参数摄动的影响都是不确定的。
为了解决这一方面的限制,提出了不需要对控制对象的动力学模型的先验知识的一种方法。
此方法是结合线性反馈律和前馈律的姿态控制器,提出的此控制器更有利于工程实现。
另外文中使用了SO(3)(李群三维旋转群)对飞行器的坐标系进行了描述。
E2.Robust global trajectory tracking for a class of underactuated vehicles SOURCE:Automatica本文提出解决了具有完全扭矩驱动和单一方向推力的这种特定类别的欠驱动飞行器的轨迹跟踪的问题。
在某些给定的假设下,提出的控制律能够跟踪平滑的参考位置轨迹,同时保证和期望姿态的角度偏差最小。
该方法在有界状态干扰的情况下可以全局地实现,即在不考虑飞行器的初始状态。
所提出的控制器在实验中使用小规模四旋翼飞行器进行测试。
文中利用混合四元数反馈策略为飞行器设计控制器。
同时,在此控制器中提出了对静态加速度扰动具有鲁棒性的积分项,并使用鲁棒的混合系统提取期望的单元四元数,并进行试验进行验证。
此文也是使用SO(3)(李群三维旋转群)对飞行器的坐标系进行了描述。
E3.Dynamics Modeling and Trajectory Tracking Control of a Quadrotor Unmanned Aerial VehicleSOURCE:Journal of Dynamic Systems, Measurement, and Control文中介绍的飞行器轨迹跟踪的功能。
关于ai英语专升本作文
关于ai英语专升本作文The integration of artificial intelligence (AI) into the field of language learning has significantly transformed the landscape of English education for students aspiring to pursue undergraduate studies. As the global demand for proficient English speakers continues to rise, AI-powered tools and applications have emerged as invaluable resources, offering personalized learning experiences and innovative approaches to language acquisition.One of the primary advantages of AI in English education is its ability to provide personalized learning pathways. Traditional classroom settings often struggle to cater to the diverse learning needs and preferences of students. AI-powered platforms can analyze individual learner profiles, identify their strengths, weaknesses, and learning styles, and then tailor the instructional content and delivery accordingly. This personalized approach not only enhances the efficiency of the learning process but also boosts student engagement and motivation.Moreover, AI-powered chatbots and virtual assistants haverevolutionized the way students practice and improve their conversational skills. These intelligent systems can engage in natural language interactions, providing real-time feedback, correcting pronunciation, and offering suggestions for grammatical improvements. This interactive experience simulates the dynamics of human-to-human communication, allowing students to develop their conversational fluency and confidence in a safe and supportive environment.Another significant benefit of AI in English education is its capacity to analyze and provide detailed feedback on written assignments. AI-powered writing assistants can identify grammatical errors, suggest sentence structure improvements, and offer guidance on vocabulary usage and tone. This comprehensive feedback empowers students to refine their writing skills, ultimately enhancing their ability to produce high-quality essays and academic papers – a crucial requirement for successful undergraduate admission.Furthermore, AI-powered adaptive learning systems can continuously monitor student progress and adjust the difficulty level of the content accordingly. As students demonstrate proficiency in certain areas, the system can automatically introduce more challenging materials, ensuring that the learning experience remains engaging and relevant. This adaptive approach helps students maintain a steady pace of progress, preventing them from becomingoverwhelmed or disinterested.In the context of undergraduate admission, AI-powered tools have also revolutionized the way students prepare for standardized English proficiency tests, such as the TOEFL and IELTS. These intelligent systems can simulate the test environment, provide practice questions, and offer detailed performance analysis. By identifying the specific areas that require improvement, students can tailor their study strategies and maximize their chances of achieving the desired test scores, a critical factor in the undergraduate admission process.Beyond academic preparation, AI-powered applications can also assist students in the college application process itself. Intelligent writing assistants can help students craft compelling personal statements, highlighting their unique experiences, strengths, and aspirations. These AI-powered tools can provide feedback on the structure, content, and tone of the essays, ensuring that the final product resonates with the admissions committee and effectively conveys the student's narrative.However, the integration of AI in English education is not without its challenges. Concerns have been raised about the potential for AI to replace human interaction and the personalized guidance provided by experienced educators. It is crucial to strike a balance between thebenefits of AI-powered tools and the irreplaceable value of human-to-human mentorship and support.To address these concerns, educational institutions and policymakers must work collaboratively to develop comprehensive frameworks that seamlessly integrate AI-powered technologies into the English learning ecosystem. This integration should be accompanied by robust teacher training programs, ensuring that educators are equipped with the necessary skills to effectively leverage AI tools and provide the necessary human touch in the learning process.In conclusion, the integration of AI in English education has profoundly transformed the landscape of language learning, particularly for students aspiring to pursue undergraduate studies. From personalized learning pathways and interactive conversational practice to comprehensive feedback on written assignments and standardized test preparation, AI-powered tools have become invaluable resources in the quest for academic success. As the technology continues to evolve, it is essential that educators, policymakers, and students work together to harness the full potential of AI while maintaining the delicate balance between technological innovation and the human element in language education.。
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Automatica42(2006)1713–1722/locate/automaticaBrief paperGlobal robust adaptive path following of underactuated shipsଁK.D.Do∗,J.PanSchool of Mechanical Engineering,The University of Western Australia,Crawley,WA6009,AustraliaReceived11April2005;received in revised form24April2006;accepted30April2006Available online7July2006AbstractWe propose a method for designing a global robust adaptive controller that forces an underactuated ship to follow a reference path under both constant and time-varying disturbances induced by waves,wind and ocean-currents.Both linear and nonlinear damping terms are included to cover both low-and high-speed applications.All nonlinear damping coefficients are assumed unknown but lie in a known compact set. The new results are derived using a choice of an appropriate body-fixed frame origin,a smooth approximation of nonsmooth damping terms, several nonlinear coordinate changes,the backstepping technique,and utilization of the ship dynamic structure.Experiments on a model ship illustrate the results.᭧2006Elsevier Ltd.All rights reserved.Keywords:Underactuated ships;Path following;Adaptive and robust control1.IntroductionSteering a ship along a desired path with prescribed forward speed is an important issue in many offshore applications.This goal can be achieved by solving trajectory-tracking and/or path-following problems(Fossen,2002).The main difficulty with both trajectory-tracking and path-following issues is that the sway axis is not actuated.This configuration is by far most com-mon among the marine surface vessels(Fossen,2002).Hence, the control problem of underactuated ships has received a lot of attention from the control community.Encarnacao,Pacoal,and Arcak(2000)described the path following errors in the Serret-Frenet frame,then designed a local path following controller under constant ocean-current.Pettersen and Nijmeijer(2001) provided a high-gain,local exponential tracking result.By ap-plying a cascade approach,a global tracking result was obtained in Lefeber,Pettersen,and Nijmeijer(2003).Two tracking so-lutions were proposed in Jiang(2002)using Lyapunov’s direct and passivity approaches.A high-gain based global practical ଁThis paper was not presented at any IFAC meeting.This paper was recommended for publication in revised form by Associate Editor Thor I. Fossen under the direction of Editor Hassan Khalil.∗Corresponding author.Tel.:+61864883602;fax:+6164881024.E-mail addresses:duc@.au(K.D.Do),pan@.au(J.Pan).0005-1098/$-see front matter᭧2006Elsevier Ltd.All rights reserved. doi:10.1016/j.automatica.2006.04.026tracking controller was developed in Behal,Dawson,Dixon, and Fang(2002)using a transformation of the ship tracking system into a skew-symmetric form.It is noted that in Jiang (2002),Pettersen and Nijmeijer(2001)and Lefeber et al.(2003), the yaw speed was required to be nonzero.This restrictive as-sumption was removed in Do,Jiang,and Pan(2002).It seems that thefirst global way-point tracking controller was proposed in Pettersen and Lefeber(2001)to force an underactuated ship to track a straight-line,see also Fredriksen and Pettersen(2004) for an improvement.Relevant independent work also includes Sira-Ramirez(1999)on differentialflatness approach.Based on a line-of-sight projection algorithm,Fossen,Breivik,and Skjetne(2003)proposed a controller to force an underactuated ship to follow a sequence of straight-line segments connected by way-points.It is noted that in Pettersen and Nijmeijer(2001),Lefeber et al.(2003),Jiang(2002),Do,Jiang,and Pan(2002),and Sira-Ramirez(1999),the mass and damping matrices of the ships are assumed to be diagonal.Relaxing these assumptions destroys the cascade structure,which is crucial for control design and stability analysis.The nonlinear damping terms are also ignored,i.e.high-speed applications are excluded.Fur-thermore,the ships usually operate in open sea subject to en-vironmental disturbances.Therefore,the control systems must consider these disturbances.Recently,we obtained a solution1714K.D.Do,J.Pan/Automatica42(2006)1713–1722 that removed the above assumptions but did not include thenonlinear damping terms(Do&Pan,2005).However whennonlinear damping terms are included in the model,we facethree difficult problems:(1)all nonlinear damping coefficientsare difficult to obtain accurately by semi-empirical methods orhydrodynamic programs;(2)nonlinear damping terms intro-duce nonsmooth terms to the model,which create a difficultywhen applying the backstepping technique;and(3)stabilityof the sway dynamics is difficult to analyze.In this paper,wepropose an approach that removes all of the aforementionedassumptions.In this approach,the derivative of the path pa-rameter is used as an additional control.The new approach isbased on a choice of an appropriate body-fixed frame origin,a smooth approximation of nonsmooth damping terms,severalnonlinear coordinate changes,thebackstepping technique,andutilization of the ship dynamic structure.2.Problem formulationAssume that the ship has an xz-plane of symmetry;heave,pitch and roll modes are neglected;the body-fixed frame co-ordinate origin is set in the center-line of the ship.Then themathematical model of an underactuated ship moving in a hor-izontal plane is described as(Fossen,2002):˙ =J( )v,M˙v=−C(v)v−(D+D n(v))v+ +J T( ) c+ w(t)(1)with=[x y ]T,v=[u v r]T, =[ u0 r]T,c=[ cu cv cr]T, w(t)=[ wu(t) wv(t) wr(t)]T,J( )= cos( )−sin( )0sin( )cos( )0001,M=m11000m22m230m32m33,C= 00C1300C23C31C320,D+D n(v)=D11000D22D230D32D33,m11=m−X˙u,m22=m−Y˙v,m23=mx g−Y˙r,m32=mx g−N˙v,m33=I z−N˙r,C13=−C31=−m22v−m22s r,C23=−C32=m11u,D11=−(X u+X|u|u|u|),D22=−(Y v+Y|v|v|v|+Y|r|v|r|), D23=−(Y r+Y|v|r|v|),D32=−(N v+N|v|v|v|+N|r|v|r|), D33=−(N r+N|v|r|v|+N|r|r|r|),where m22s=0.5(m23+m32),(x,y, )denote the position and orientation of the ship with coordinates in the earth-fixed frame; u,v and r denote surge,sway and yaw speeds with coordinates in the body-fixed frame;m is the mass of the ship;I z is theYydy dFig.1.Interpretation of path following errors.ship’s inertia about the Z b-axis of the body-fixed frame;x g is the X b-coordinate of the ship center of gravity in the body-fixed frame(see Fig.1);the controls u and r are the surge force and yaw moment in the body-fixed frame; c and w(t)are constant and time-varying disturbances.Notice that we use w(t)in the body-fixed frame to shorten later expressions.Treating w(t)in the earth-fixed frame isthe same but only the magnitude of w(t) is different.The other symbols are referred to as hydrodynamic derivatives(SNAME,1950).Control objective:Under Assumptions1and2,design the controls u and r to force the ship(1)to follow a prescribed path parameterized by(x d(s),y d(s))with s being the path parameter in the sense that the position of the ship(1)tracks the path ,the ship total velocity is tangential to the path , and let the desired surge speed,u0(t),be adjustable.Assumption1.(1)The ship parameters,added masses and lin-ear damping coefficients:m,I z,X˙u,Y˙v,Y˙r,N˙v,X u,Y v,Y r,N v, N r are known.(2)The nonlinear damping coefficients:X|u|u,Y|v|v,Y|r|v, Y|v|r,N|v|v,N|r|v,N|v|r,N|r|r are unknown but lie in a known compact set,i.e.there exist known positive constants X|u|u, Y|v|v, Y|r|v, Y|v|r, N|v|v, N|r|v, N|v|r, N|r|r such that|X|u|u| X|u|u,|Y|v|v| Y|v|v,|Y|r|v| Y|r|v,|Y|v|r| Y|v|r,|N|v|v| N|v|v,|N|r|v| N|r|v,|N|v|r| N|v|r,|N|r|r| N|r|r. (3)The disturbance forces and moments are unknown but lie in a known compact set,i.e.there exist known positive constants M cu, M cv, M cr, M wu, M wv, M wr such that| cu| M cu,| cv| M cv,| cr| M cr,| wu(t)| M wu,| wv(t)| M wv,| wr(t)| M wr,∀t∈R+.(4)There exist strictly positive constants¯u∗d,u∗0,u∗01,u∗02and u M0such thatx d2(s)+y d2(s) ¯u∗d,∀s∈R,u∗0 u0(t) u M0,|˙u0(t)| u∗01,|¨u0(t)| u∗02,∀t∈R+.(2)K.D.Do,J.Pan /Automatica 42(2006)1713–17221715Remark 1.Several software packages (such as VERES from Marintek)can be used to calculate all the ship parameters,added masses and linear damping coefficients accurately.How-ever,the coefficients of nonlinear damping terms are difficult to obtain accurately.Therefore Parts (1)and (2)of Assumption 1are reasonable.Part (4)implies that the path is regular with respect to the path parameter s .The desired surge speed is always strictly positive,i.e.we do not consider stabiliza-tion/regulation problems.Assumption 2.There exist strictly positive constants k 1,k 2,|v |r , |r |v ,b ∗i , i,i =1,2,3such that one of the following sets of conditions holds:(1)Set I :1−m 11m −122−[0.65m −122(( Y|v |r (1+ |v |r )+ Y |r |v (1+ |v |r ))]2−{k 2+m −122×(k 1m 11+1.3k 2[(( Y |v |r (1+ |v |r )+ Y |r |v (1+ |v |r )]−Y r )}/u ∗0 b ∗1,Y |r |v +|Y |v |r | − 1.(2)Set II :1−m 11m −122−0.65m −122(0.5+k 2/u ∗0)[( Y |v |r (1+ |v |r )+ Y |r |v (1+ |v |r )]−(k 2+k 1m 11m −122−Y r m −122)/u ∗0 b ∗2,Y |v |v +(Y |r |v −|Y |v |r |)|¯r d (s)|/(b ∗2¯u d (s)) − 2,∀s ∈R .(3)Set III :X |u |u =Y |v |v =Y |r |v =Y |v |r =N |v |v =N |r |v =N |v |r=N |r |r =0,1−m 11m −122−(k 2+m 11k 1m −122−Y r m −122)/u ∗0 b ∗3,Y v +(m 11(k 1+u M 0)+|Y r |)|¯r d (s)|/(b ∗2¯u d (s)) − 3,s ∈RWe have defined ¯r d (s)and ¯u d (s)as¯u d (s)=x d 2(s)+y d2(s),¯r d (s)=(x d (s)y d (s)−x d (s)y d(s))¯u 2d (s),(3)where (•) =j (•)/j s and (•) =j 2(•)/j s 2.From nowonward,we drop the argument s of ¯r d (s)¯u d (s).Remark 2.The first conditions of Sets I and II,and the second condition of Set III are needed so that the control problem is solvable.The second conditions of Sets I and II,and the third condition of Set III are needed to guarantee boundedness of the sway velocity.The first conditions of Sets I and II imply that:the control design parameters (k 1,k 2, |v |r , |r |v )cannot be arbitrarily large,u ∗0should not be too small,|Y |v |r |and |Y |r |v |are not too large compared with m 22,and m 11<m 22.The main differences between Sets I and II are:Set I allows bigger value of |Y |v |r |and |Y |r |v |,and arbitrarily large path curvaturebut requires Y |r |v <|Y |r |v |.On the other hand Set II does notrequire Y |r |v <|Y |v |r |but cannot allow the path curvature (viathe ratio ¯r d /¯ud )to be arbitrarily large.Set III implies that when the nonlinear damping terms are neglected,the curvature of the desired path is restricted by Y v and Y r .The main ideas to solve the control objective are:1.Choose an appropriate body-fixed frame origin to avoid the yaw moment r acting directly on the sway dynamics.2.Interpret path-following errors in a frame attached to the path such that the error dynamics are of a triangular form to which the backstepping technique can be applied.e the orientation error as a virtual control to stabilize the cross-track error where the derivative of the path parameter is used as a control to design a global controller.e smooth approximations of |r |and |v |when applying the backstepping technique to avoid the use of nonsmooth control design techniques.3.Coordinate transformations 3.1.Choosing body-fixed frame originTo avoid the yaw moment r acting directly on the sway dynamics,we choose the body-fixed frame origin such that it is on the center-line of the ship (see Fig.1),i.e.y g =0and that y g =Y ˙r /m .It is noted that although this choice of the body-fixed frame origin is somewhat uncertain due to the uncertainty of Y ˙r ,our experimental results show that the proposed controller can handle this uncertainty very well.As such,the model (1)becomes˙x =u cos ( )−v sin ( ),˙y =u sin ( )+v cos ( ),˙=r ,˙v =m −122(−m 11ur +(Y r +Y |v |r |v |)r +(Y |r |v |r |+Y v +Y |v |v |v |)v−sin ( ) cu +cos ( ) cv + wv (t)),˙u =m −111[(m 22v +m 22s r)r +(X u +X |u |u |u |)u + u+cos ( ) cu +sin ( ) cv + wu (t)],˙r =m 32m −122m −133[m 11ur −(Y r +Y |v |r |v |)r −(Y |r |v |r |+Y v +Y |v |v |v |)v +sin ( ) cu −cos ( ) cv− wv (t)]+m −133[−(m 22v +m 22s r)u +m 11uv +(N v +N |v |v |v |+N |r |v |r |)v +(N r +N |v |r |v |+N |r |r |r |)r + r + cr + wr (t)].(4)3.2.Transforming path-following errorsWe now interpret the path-following errors in a frame at-tached to the path (Samson,1991)as (see Fig.1):[x ey ee ]T =J T ( )[x −x d y −y d − d ]T(5)1716K.D.Do,J.Pan/Automatica42(2006)1713–1722 where d is the angle between the path and the X-axis definedby d=arctan(y d(s)/x d(s))with x d(s)and y d(s)defined in(3).In Fig.1,OXY is the earth-fixed frame;O p X p Y p is a frameattached to the path such that O p X p and O p Y p are parallelto the surge and sway axes of the ship;u d is tangential to thepath;O c is the center of gravity of the ship;and O b X b Y b isthe body-fixed frame.Therefore x e,y e and e are referred to astangential,cross and heading errors,respectively.Differentiat-ing both sides of along the solutions of thefirst three equationsof results in the kinematic error dynamics:˙x e=u−u d cos( e)+ry e,˙y e=v+u d sin( e)−rx e,˙e=r−r d,(6)where u d and r d are given byu d=¯u d˙s,r d=¯r d˙s(7)with¯u d and¯r d given in(3).From(6),it can be seen that(x e,y e, e)=(0,0,0)is the equilibrium point if only the swayvelocity is zero,i.e.v=0,which means that the ship must moveon a straight-line at the steady state.Since we allow the pathto be different from a straight-line,but also include it,the swayvelocity is generally different from zero at the steady state.InDo and Pan(2005),we resolved this difficulty by introducingan angle to the orientation error ing that approach,wefind it impossible to include the nonlinear damping terms.Thispaper presents a different approach to design u and r in thenext section.4.Control designFor convenience of the reader,we divide the control designprocedure into two separate stages.Firstly,we consider(6)andthefifth equation of(4).Secondly,the last two equations of(4)are considered to design the controls u and r using thebackstepping technique found in Khalil(2002).4.1.Kinematic control designSub-step1.1.Stabilizing(x e,y e)-dynamics:Defineu e=u− u,¯ e= e−e,(8)where u ande are virtual controls of u and e.Substituting(8)into thefirst two equations of(6)gives˙x e= u+u e−u d cos(e )+ry e+ 1,˙y e=v+u d sin(e)−rx e+ 2,(9) where1=−u d((cos(¯ e)−1)cos(e )−sin(¯ e)sin(e)),2=u d(sin(¯ e)cos(e )+(cos(¯ e)−1)sin(e)).(10)From thefirst equation of(9),we design a control law for uto stabilize the x e-dynamics asu=− 1+u d cos(e), 1=k1x e/ , =1+x2e+y2e,(11)where k1is a positive constant,which is chosen as in As-sumption2.Stabilizing the second equation of(9)is more in-volved.We cannot directly useeto cancel v since the termu d sin(e)cannot cancel v without restriction on the initialconditions and on the size of the disturbances in the sway dy-namics.The idea to overcome this difficulty is to design u d insuch a way that it guarantees the desired surge velocity at thesteady state,and dominates v.Hence,we use the derivative ofthe path parameter as a control designed as˙s=u20+( 2+v)2/¯u d, 2=k2y e/ ,(12)where k2is a positive constant chosen as in Assumption2.With(12),we haveu d=u20+( 2+v)2.(13)From(12),the second equation of(9),and(7),we design acontrol law forease=−arctan(( 2+v)/u0).(14)Substituting(14),(12)and(11)and into(9)results in˙x e=− 1+u e+ry e+ 1,˙y e=− 2−rx e+ 2.(15)On the other hand,substituting(14)into(11)results inu=− 1+u0.(16)Remark3.At the steady state,with the help of a control(tobe designed later),convergence of(x e,y e,¯ e,u e)to an ar-bitrarily small value implies that e converges to0.5( ,− )plus/minus an arbitrarily small value.This implies that the shipwill not turn around.From(16),the surge velocity u(t)willapproach u0plus/minus an arbitrarily small value,and u d(t)will approach the total linear speed:u20+v(t)2plus/minus anarbitrarily small value.Sub-step1.2.Stabilizing¯ e dynamics:Differentiating bothsides of the second equation of(8)along the solutions of(15)and the fourth equation of(4)results in˙¯e=r−r d−˙u0( 2+v)u d−k2u0x eu dr+k2u0u2d 3[(1+x2e)(− 2+ 2)−x e y e(− 1+ 1)]−k2u0x e y e u eu2d 3+u0m22u2d[−m11ur+(Y r+Y|v|r|v|)×r+(Y|r|v|r|+Y v+Y|v|v|v|)v−sin( ) cu+cos( ) cv+ wv(t)].(17)K.D.Do,J.Pan /Automatica 42(2006)1713–17221717From (17),if r is considered as a control to stabilize the ¯ edynamics,we face a nonsmooth problem due to |r |and |v |.The nonsmooth control design technique in Tanner and Kyriakopoulos (2003)can be modified and applied to our case,but computing Filippov’s differential inclu-sion and nonsmooth stability analysis are required.We here use a simple way to get around the problem.Adding andsubtracting the terms u 0m −122u −2d ¯Y |v |r vr tanh (¯ e vr/( 1 3)),u 0m −122u −2d ¯Y |r |v vr tanh (¯ e vr/( 2 3)),u 0m −122u −2d ¯Y |v |v v 2tanh (¯ e v 2/( 3 3)), M wv m −122u −2d tanh (¯ e /( 4 3)),where ¯Y|v |r =|Y |v |r |,¯Y |r |v =|Y |r |v |,¯Y |v |v =|Y |v |v |,and i ,1 i 4are small positive constants to be specified later,to the right hand side of (17)result in˙¯ e =b 1−r d +u 0m −122u −2d[¯Y |v |v v 2tanh (¯ e v 2/( 3 3))−sin ( ) cu +cos ( ) cv + M wvtanh (¯e/( 4 3))+˜¯Y |v |r vr tanh (¯ evr/( 1 3))+˜¯Y |r |v vr tanh (¯ evr/( 2 3))]+b 2u e + 1+ 3(18)where b 1=r −k 2u 0x e u dr −u 0m 22u d [m 11 u r +Y r r +ˆ¯Y |v |r vr tanh (¯ evr/( 1 3))+ˆ¯Y|r |vvr tanh (¯ evr/( 23))],b 2=−k 2u 0x e y e u −2d −3−m 11u 0rm −122u −2d , 3=−˙u 0( 2+v)u −2d+k 2u 0u −2d −3((1+x 2e )(− 2+ 2)−x e y e (− 1+ 1))+Y v u 0vm −122u −2d ,1=u 0m −122u −2d [Y |v |r |v |r −¯Y |v |r vr tanh (¯ e vr/( 1 3))+Y |r |v |r |v −¯Y |r |v vr tanh (¯ evr/( 2 3))+Y |v |v |v |v −¯Y|v |v v 2tanh (¯ ev 2/( 3 3))+ wv (t)− M wv tanh (¯ e /( 4 3))](19)where (ˆ•)denotes an estimate of (•),and (˜•)=(•)−(ˆ•).Theˆ¯Y|v |r and ˆ¯Y |r |v estimates are updated as follows:˙ˆ¯Y |v |r = 1proj (¯ e vr tanh (¯ evr/( 1 3)),ˆ¯Y |v |r ),˙ˆ¯Y |r |v = 2proj (¯ e vr tanh (¯ evr/( 2 3)),ˆ¯Y |r |v ),(20)where 1and 2are positive constants to be specified later.The operator proj represents the Lipschitz projection algorithm (Pomet &Praly,1992)asproj ( ,ˆ )= if (ˆ ) 0,proj ( ,ˆ )= if (ˆ ) 0and ˆ (ˆ ) 0,proj ( ,ˆ )=(1− (ˆ )) if (ˆ )>0and ˆ (ˆ) >0,where (ˆ )=(ˆ 2− 2M )/( 2+2 M ), ˆ(ˆ )=j (ˆ )/j ˆ , is an arbitrarily small positive constant (ASPC),and | | M .The projection algorithm is such that if ˙ˆ =proj ( ,ˆ )andˆ (t 0) M then (a)ˆ (t) M + ,∀0 t 0 t <∞;(b)proj ( ,ˆ )is Lipschitz continuous;(c)|proj ( ,ˆ )| | |;(d)˜ proj ( ,ˆ ) ˜ with ˜ = −ˆ .The reason we use the above projection algorithm is to guar-antee boundedness of ˆ¯Y|r |v and ˆ¯Y |v |r ,i.e.ˆ¯Y |v |r Y |v |r (1+ |v |r ),ˆ¯Y |r |v Y |r |v (1+ |r |v ),(21)where |v |r and |r |v are ASPCs.From (18),we consider b 1asa control to stabilize the ¯ e dynamics.Define r e =b 1− r ,(22)where r is a virtual control of b 1.We substitute (22)into (18)and choose the virtual control r asr =−k 3¯ e +r d −u 0m −122u −2d[ˆ¯Y |v |v v 2tanh (¯ e v 2/( 3 3))−sin ( )ˆ cu +cos ( )ˆcv + M wvtanh (¯ e/( 4 3))]− 3,(23)where k 3is a positive constant.The estimates ˆ cu ,ˆ cv will beupdated later.The estimate ˆ¯Y|v |v is updated as follows:˙ˆ¯Y |v |v = 3proj (¯ e v 2tanh (¯ ev 2/( 3 3)),ˆ¯Y |v |v ),(24)where 3is a positive constant to be specified later.It is notedthat (24)guarantees that ˆ¯Y |v |v Y |v |v (1+ |v |v ),(25)where |v |v is an ASPC.Substituting (22)and (23)into (18)results in˙¯ e =−k 3¯ e+r e +b 2u e +u 0m −122u −2d[˜¯Y |v |r vr tanh (¯ evr/( 1 3))+˜¯Y |r |v vr tanh (¯ evr/( 2 3))+˜¯Y|v |vv 2tanh (¯ ev 2/( 33)]−sin ( )˜ cu +cos ( )˜ cv )+ 1.(26)4.2.Kinetic control design4.2.1.Design uDifferentiating both sides of the first equation of (8)along the solutions of the first two equations of (6),and the fifth equation of (4)allows the control u to be designed as u =−m 11(k 4u e +k 5r 2u e −˙ u +b 2¯ e)−X u u −(m 22v +m 22s r)r −ˆX|u |u |u |u −cos ( )ˆ cu −sin ( )ˆ cv − M wu tanh (u e /( 5 3)),(27)1718K.D.Do,J.Pan/Automatica42(2006)1713–1722 where k4,k5are positive constants, 5is an ASPC to be specifiedlater.With(27),we have the u e dynamics as˙u e=−k4u e−k5r2u e−b2¯ e+m−111[X u u e+˜X|u|u|u|u+cos( )˜ cu+sin( )˜ cv+ wu(t)− M wu tanh(u e/( 5 3))].(28)4.2.2.Design rDifferentiating(22)along the solutions of(6),the fourth andsixth equations of(4)results in˙r e=j b1m33j rm32m22(m11ur−(Y r+Y|v|r|v|)r−(Y|r|v|r|+Y v+Y|v|v|v|)v+sin( ) cu−cos( ) cv− wv(t))−(m22v+m22s r)u+m11uv+(N v+N|v|v|v| +N|r|v|r|)v+(N r+N|v|r|v|+N|r|r|r|)r+ r+ cr+ wr(t)−j rj x e˙x e−j rj y e˙y e−j rj e˙e−j rj˙ −j rj s˙s−j rj u0˙u0−j rj˙u0¨u0−j rjˆ¯Y|v|v˙ˆ¯Y|v|v−j rjˆ cu˙ˆcu−j rjˆ cv˙ˆcv+j b1j x e˙x e+j b1j y e˙y e+j b1j e˙e+j b1j s˙s+j b1j u0˙u0+j b1jˆ¯Y|v|r˙ˆ¯Y|v|r+j b1jˆ¯Y|r|v˙ˆ¯Y|r|v−1m22j rj v−j b1j v[−m11ur+(Y r+Y|v|r|v|)r+(Y|r|v|r|+Y v+Y|v|v|v|)v−sin( ) cu+cos( ) cv+ wv(t)].(29)We now evaluate j b1/j r and make sure that this term is invert-ible.From the expression of b1(see(19)),we havej b1/j r=1−k2u0u−2d −1x e−m−122u−2d u0[m11 u+Y r+ˆ¯Y|v|r v(vr¯e/( 13))1+cosh2(¯ e vr/( 1 3))+ˆ¯Y|r|v v(vr¯e/( 23))1+cosh2(¯ e vr/( 2 3))+ˆ¯Y|v|r v tanh(¯ e vr/( 1 3))+ˆ¯Y|r|v v tanh(¯ e vr/ 2)].(30)A simple calculation shows thatj b1/j r 1−k2u−2d u0−u0m−122u2d(m11(k1+u0)+Y r+1.3(ˆ¯Y|v|r+ˆ¯Y|r|v)(|v+ 2|+ 2)).(31) Substituting(21)into(31)shows thatj b1/j r u∗20u−2d b∗1,if1st condition of Set I holds,j b1/j r b∗2,if1st condition of Set II holds,j b1/j r b∗3,if1st and2nd conditions of Set III holds.(32)Considering(32),we choose the control r from(29)asr=(m22v+m22s r)u−m11uv−(N v+ˆN|v|v|v|+ˆN|r|v|r|)v −(N r+ˆN|v|r|v|+ˆN|r|r|r|)r−ˆ cr− M wr tanh((j b1/j r)r e/( 6 3))−m32m−122(m11ur−(Y r+ˆY|v|r|v|)r−(ˆY|r|v|r|+Y v+ˆY|v|v|v|)v+sin( )ˆ cu−cos( )ˆ cv)+m33(j b1/j r)−1−k6r e+j rj x e˙x e+j rj y e˙y e+j rj e˙e+j rj˙ +j rj s˙s+j rj u0˙u0+j rj˙u0¨u0+j rjˆ¯Y|v|v˙ˆ¯Y|v|v+j rjˆ cu˙ˆcu+j rjˆ cv˙ˆcv−j b1j x e˙x e −j b1j y e˙y e−j b1j e˙e−j b1j s˙s−j b1j u0˙u0−j b1jˆ¯Y|v|r˙ˆ¯Y|v|r−j b1jˆ¯Y|r|v˙ˆ¯Y|r|v+1m22j rj v−j b1j v×(−m11ur+(Y r+ˆY|v|r|v|)r+(ˆY|r|v|r|+Y v+ˆY|v|v|v|)v−sin( )ˆ cu+cos( )ˆ cv)−m33 M wv 2tanh( 2r e/( 7 3),(33)where 2=j b1/j r+m32m−122m−133+m−122(j r/j v−j b1/j v); 6and 7are small positive constants to be specified later.Sub-stituting(33)into(29)results in˙r e=−k6r e−¯ e+(j b1/j r)m−133[m32m−122(−˜Y|v|r|v|r−˜Y|r|v|r|v −Y|v|v|v|v+sin( )˜ cu−cos( )˜ cv− wv(t))+˜N|v|v|v|v+˜N|r|v|r|v+˜N|v|r|v|r+˜N|r|r|r|r+˜ cr+ wr(t))−m−122×(j r/j v−j b1/j v)(˜Y|v|r|v|r+˜Y|r|v|r|+˜Y|v|v|v|v−sin( )˜ cu+cos( )˜ cv+ wv(t))− M wr tanh((j b1/j r)r e/( 6 3))− M wv 2tanh( 2r e/( 7 3))].(34) The estimatesˆX|u|u,ˆY|v|r,ˆY|r|v,ˆY|v|v,ˆN|v|v,ˆN|r|v,ˆN|v|r,ˆN|r|r,ˆcu,ˆ cv,ˆ cr are updated as follows:˙ˆX|u|u= 4proj(u e|u|u,ˆX|u|u),˙ˆY|v|r= 5proj(−r e m−122(m32m−133j b1/j r+(j r/j v−j b1/j v))|v|r,ˆY|v|r),˙ˆY|r|v= 6proj(−r e m−122(m32m−133j b1/j r+(j r/j v−j b1/j v))|r|v,ˆY|r|v),˙ˆY|v|v= 7proj(−r e m−122(m32m−133j b1/j r+(j r/j v−j b1/j v))|v|v,ˆ¯Y|v|v),˙ˆN|v|v= 8proj(r e m−133j b1/j r|v|v,ˆN|v|v),K.D.Do,J.Pan/Automatica42(2006)1713–17221719˙ˆN|r|v= 9proj(r e m−133j b1/j r|r|v,ˆN|r|v),˙ˆN|v|r= 10proj(r e m−133j b1/j r|v|r,ˆN|v|r),˙ˆN|r|r= 11proj(r e m−133j b1/j r|r|r,ˆN|r|r),˙ˆcu= 12proj(u e m −111cos( )+m−122(r e(m32m−133j b1/j r+j r/j v−j b1/j v)−u0u−2d¯ e)sin( ),ˆ cu),˙ˆcv= 13proj(u e m −111sin( )−m−122(r e(m32m−133j b1/j r+j r/j v−j b1/j v)−u0u−2d¯ e)cos( ),ˆ cu),˙ˆcr= 14proj(r e m −133j b1/j r,ˆ cr),(35)where i,4 i 14are positive constants to be specified later. It is noted that guaranteesˆX|u|u X|u|u(1+ |u|u),ˆY|v|r Y|v|r(1+ |v|r),ˆY|r|v Y|r|v(1+ |r|v),ˆY|v|v Y|v|v(1+ |v|v),ˆN|v|v N|v|v(1+ N|v|v),ˆN|r|v N|r|v(1+ N|r|v),ˆN|v|r N|v|r(1+ N|v|r),ˆN|r|r N|r|r(1+ N|r|r),ˆ cu M cu(1+ cu),ˆ cv M cv(1+ cv),ˆ cr M cr(1+ cr)(36) where |u|u, N|v|v, N|r|v, N|v|r, N|r|r, cu, cv, cr are ASPCs. We now present our main result whose proof is given in Ap-pendix A.Theorem1.Assume that Assumptions1and2hold,the con-trols u and r given by(27)and(33),and the adaptation laws given by(20),(24)and(35)solve the control objective. All signals of the closed loop system consisting of(15),(26), (28),(34)are bounded.Particularly,the transformed path-following errors(x e,y e,¯ e)globally asymptotically converge to a ball centered at the origin.The radius of this ball canbe adjustable by adjusting the control parameters k j,1 j 6, i,1 i 7, |v|r, |r|v,and the adaptation gains i,1 i 14. As a result,the actual position path following errors,(x−x d,y−y d),globally asymptotically converge to a ball cen-tered at the origin with an adjustable radius,and the ship will never turn around.Furthermore,the desired forward speed of the ship on the path can be adjusted by adjusting u0(t) and the total linear velocity of the ship is tangential to the path.5.Experimental resultsThis section describes the experimental setup and tested re-sults performed on a model ship(Fig.2)with a length of1.2m and a mass of17.5kg in the Swan river,Western Australia. The ship is equipped with:a differential global positioning sys-tem(DGPS)SF_2050G,one compass TCM2-50to obtain po-sition and orientation,two propellers,and two sets ofwirelessFig.2.An inside view of the model ship. transceivers to transmit and receive signals between the ship and the host computer.Based on our calculation and previ-ously executed empirical experiments,the parameters of our model ship are m11=25.8,m22=33.8,m33=2.76,m23= m32=6.2,D11=12+2.5|u|,D22=17+4.5|v|,D23=0.2, D32=0.5,D33=0.5+0.1|r|.These parameters are used in the controller and observer design,and implementation pre-sented in this section.All the nonlinear off-diagonal terms are ignored.A high-gain observer(Atassi&Khalil,1999)is used to estimate the velocities.We chose thehigh-gain observer be-cause the combination of global state feedback control with a high-gain observer allows for a separation approach where the high-gain isfirst designed to meet the control objectives,then the high-gain observer is designed,sufficiently fast to recover the performance achieved under state feedback.The high-gain observer is designed as follows:˙ˆ =J( )ˆv+H1( −ˆ ),M˙ˆv=−C(ˆv)ˆv−(D+D n(ˆv))ˆv+ +J T( )H2( −ˆ ),(37)whereˆ =[ˆxˆyˆ ]T andˆv=[ˆuˆvˆr]T are estimates of and v,respectively;H1=diag(ϑ−1,ϑ−1,ϑ−1)and H2=diag(h21ϑ−2,h22ϑ−2,h23ϑ−2)withϑa small positive constant,and h2i>1,i=1,2,3.The reference path is cho-sen as x d(s)=s,y d(s)=10sin(0.1s)+20,and u d0=0.6.The control design constants are k1=0.1,k2=0.05,k3=2,k4= 4,k5=0.1,k6=4, i=0.02,1 i 7, j=150,1 j 14, and all other“ ”are0.001.With this choice,Assumption1 holds with¯u∗d=10,u∗0=u M0=0.6,u∗01=u∗02=0,and Set II of Assumption2holds with b∗2=0.016, 2=4.5.The observer gains areϑ=0.1,h2i=3,i=1,2,3.The initial con-ditions are x(0)=−5m,y(0)=5m, (0)=0.009rad,u(0)= v(0)=0m/s,r(0)=0rad/s,ˆx(0)=−5m,ˆy(0)=5m,ˆ (0)=0.009rad,ˆu(0)=ˆv(0)=m/s,ˆr(0)=0rad/s.The es-timates of the nonlinear damping coefficients are initialized at 0.5of their values obtained by our previously executed empir-ical experiments as mentioned above.Experimental results are given in Figs.3and4where the ship position and orientation in the xy plane,and the position and orientation path following errors are plotted.These errors actually do not converge to zero mainly due to errors of the DGPS and compass.。