Digital Control of Dynamic Systems 动态系统的数字控制实验答案
智能网联汽车云控系统及其实现
汽车工程Automotive Engineering2020年(第42卷)第12期2020(Vol.42)No.12 doi:10.19562/j.chinasae.qcgc.2020.12.001智能网联汽车云控系统及其实现李克强1,常雪阳「,李家文2,许庆1,高博麟「,潘济安1(1.清华大学,汽车安全与节能国家重点实验室,北京100084; 2.启迪云控(北京)科技有限公司,北京100084)[摘要]本文中提岀了基于信息物理系统(cyber-physical system,CPS)理论的智能网联汽车云控系统概念,该系统利用新一代信息与通信技术,将人、车、路、云的物理层、信息层、应用层连为一体,进行融合感知、决策与控制,可实现车辆行驶和交通运行安全、效率等性能的综合提升。
在介绍系统架构、工作原理与关键技术的基础上,研究了边缘云上融合感知技术与时变时延下车辆控制技术,开发了面向真实道路的云控系统。
通过仿真与道路试验,验证了系统的云端计算、融合感知、融合决策与网联控制的性能,展示了系统实际应用的可行性与先进性。
关键词:智能网联汽车;云控系统;信息物理系统;融合感知;网联车辆控制Cloud Control System for Intelligent and Connected Vehicles and Its ApplicationLi Keqiang1,Chang Xueyang1,Li Jiawen2,Xu Qing1,Gao Bolin1&Pan Jian11.Tsinghua University,State Key厶aboratory of Automotive Safety and Energy,Beijing100084;2.TUS Cloud Control(Beijing)Technology Co.,Ltd.,Beijing100084[Abstract]In this paper,the concept of cloud control system for intelligent and connected vehicles is proposed based on the theory of cyber-physical system(CPS).The system uses the new generation of information and communication technologies to integrate the physical layer,cyber layer and application layer of human,vehicles, road infrastructures and cloud for integrated perception,decision-making and control to realize comprehensive improvement of vehicles and traffic safety and efficiency.Based on the introduction of the system architecture,working principle and key technologies,integrated perception technology on edge cloud and vehicle control technology under time-varying delay are studied.Furthermore,a cloud control system for real road is developed.Simulation and field test results verify the performance of cloud computing,integrated perception,decision-making,and connected control of the proposed system,which demonstrates its feasibility and superiority in application.Keywords:intelligent connected vehicles;cloud control system;cyber-physical system;integrated perception;networked vehicle control前言自动驾驶是汽车与交通领域的颠覆性技术,正引发学术界和工业界开展广泛且深入的研究。
《自动控制原理》课程主要参考教材
《自动控制原理》课程主要参考教材自动控制原理(第四版)【作者】胡寿松【出版社】科学出版社【出版时间】2001.2【内容简介】本书系《自动控制原理》一书的第四版,比较全面地阐述了自动控制的基本理论与应用。
全书共分十章,前八章着重介绍经典控制理论及应用,后两章介绍现代控制理论中的线性系统理论和最优控制理论。
本书精选了第三版中的主要内容,加强了对基本理论及其应用的阐述。
书中深入浅出地介绍了自动控制的基本概念,控制系统在时域和复域中的数学模型及其结构图和信号流图;比较全面地阐述了线性控制系统的时域分析法、根轨迹法、频域分析法以及校正和设计等方法;对线性离散系统的基础理论、数学模型、稳定性及稳态误差、动态性能分析以及数字校正等问题,进行了比较详细的讨论;在非线性控制系统分析方面,给出了相平面和描述函数两种常用的分析方法,对目前应用日益增多的非线性控制的逆系统方法也作了较为详细的介绍;最后两章根据高新技术发展的需要系统地阐述了线性系统的状态空间分析与综合,以及动态系统的最优控制等方法。
书末给出的两个附录,可供读者在学习本书的过程中查询之用。
本书1985年被评为航空工业部优秀教材,1988年被评为全国优秀教材,1997年被评为国家级教学成果二等奖,同年被批准列为国家“九五”重点教材。
本书可作为高等工业院校自动控制、工业自动化、电气自动化、仪表及测试、机械、动力、冶金等专业的教科书,亦可供从事自动控制类的各专业工程技术人员自学参考。
自动控制原理(第五版)【作者】胡寿松【出版社】科学出版社【出版时间】2007.6【内容简介】《自动控制原理》(第5版)精选了第四版中的主要内容,加强了对基本理论及其工程应用的阐述。
书中深入浅出地介绍了自动控制的基本概念,控制系统在时域和复域中的数学模型及其结构图和信号流图;比较全面地阐述了线性控制系统的时域分析法、根轨迹法、频域分析法以及校正和设计等方法;对线性离散系统的基础理论、数学模型、稳定性及稳态误差、动态性能分析以及数字校正等问题,进行了比较详细的讨论;在非线性控制系统分析方面,给出了相平面和描述函数两种常用的分析方法,对目前应用日益增多的非线性控制的逆系统方法也作了较为详细的介绍;最后两章根据高新技术发展的需要,系统地阐述了线性系统的状态空间分析与综合,以及动态系统的最优控制等方法。
Intelligent Control Systems
Intelligent Control Systems Intelligent Control Systems are a crucial aspect of modern technological advancements, playing a significant role in various industries such as automotive, aerospace, manufacturing, and robotics. These systems are designed to autonomously make decisions and control processes, ultimately improving efficiency, accuracy, and productivity. However, they also present a unique set of challenges and considerations that need to be addressed to ensure their successful implementation and operation. One of the primary challenges of intelligent control systems isthe complexity of the algorithms and software required to enable autonomous decision-making. Developing these intricate systems demands a high level of expertise in fields such as artificial intelligence, machine learning, and control theory. Moreover, the integration of these systems into existing infrastructurecan be a daunting task, requiring careful planning and execution to avoid disruptions and malfunctions. Another critical consideration is the ethical implications of intelligent control systems, particularly in applications where human safety is at stake. For instance, in autonomous vehicles, these systems must be programmed to make split-second decisions in potentially life-threatening situations. This raises questions about accountability and the moral implications of allowing machines to make decisions that could impact human lives. Furthermore, the reliability and robustness of intelligent control systems are paramount. These systems must be able to adapt to unforeseen circumstances and continue to operate effectively in dynamic environments. Failures in these systems can lead to costly downtime, safety hazards, and damage to equipment. Therefore, rigorous testing and validation procedures are essential to ensure the dependability of intelligent control systems. In addition to technical challenges, the implementation of intelligent control systems may also face resistance from workers who fear being replaced by automation. This fear is not unfounded, as the integration ofintelligent control systems may lead to a reduction in the demand for human laborin certain tasks. It is crucial for organizations to address these concerns and emphasize the collaborative nature of human-machine interaction, where intelligent control systems complement human capabilities rather than replace them. Despite these challenges, the potential benefits of intelligent control systems areundeniable. These systems have the capacity to revolutionize industries by improving efficiency, reducing human error, and enabling tasks that are beyond human capabilities. For example, in manufacturing, intelligent control systems can optimize production processes, minimize waste, and enhance product quality, ultimately leading to cost savings and competitive advantages. In conclusion, intelligent control systems hold great promise for the future of technology and industry. However, their implementation and operation present a myriad of challenges that need to be carefully considered and addressed. From technical complexities to ethical implications and human concerns, a holistic approach is necessary to ensure the successful integration of intelligent control systems. By acknowledging these challenges and working towards innovative solutions, we can harness the full potential of intelligent control systems while mitigating their associated risks.。
Optimization and Control of Dynamic Systems
Optimization and Control of DynamicSystemsOptimization and control of dynamic systems are essential in various fields, including engineering, economics, and biology. These systems involve complex interactions and behaviors that require careful management to achieve desired outcomes. In this discussion, we will explore the challenges and approaches to optimizing and controlling dynamic systems from multiple perspectives, considering the technical, ethical, and practical implications of these processes. From a technical standpoint, optimizing and controlling dynamic systems often involves dealing with nonlinearities, uncertainties, and time-varying dynamics. These complexities pose significant challenges for engineers and researchers who seek to design effective control strategies. For example, in the field of robotics, controlling the motion of a humanoid robot requires accounting for dynamic interactions with the environment, sensor noise, and the robot's own flexibility. This necessitates the development of advanced control algorithms, such as model predictive control or reinforcement learning, to adapt to changing conditions and achieve optimal performance. Moreover, the optimization of dynamic systems is not limited to purely technical considerations. Ethical and societal implications also come into play, particularly when considering autonomous systems and artificial intelligence. For instance, in the context of autonomous vehicles, optimizing control systems must prioritize safety and ethical decision-making, such as in situations where a collision is unavoidable. This raises important questions about the ethical programming of such systems and the potential impact on human lives. As we continue to integrate autonomous technologies into everyday life, theethical dimension of optimization and control becomes increasingly critical. Beyond the technical and ethical aspects, the practical implementation of optimization and control strategies in dynamic systems presents its own set of challenges. Real-world systems often have constraints and limitations that must be accounted for in the design of control algorithms. In industrial processes, for example, optimizing the operation of complex manufacturing systems requires balancing competing objectives such as production efficiency, energy consumption,and equipment longevity. This calls for a holistic approach that considers not only the technical aspects of control, but also the economic and environmental implications of system optimization. Furthermore, the advancement of optimization and control techniques for dynamic systems is closely tied to ongoing research and innovation. As new technologies emerge and our understanding of complex systems deepens, there is a continuous need to develop more sophisticated control algorithms and optimization methods. This requires collaboration acrossdisciplines and a commitment to lifelong learning and professional development. Engineers and researchers must stay abreast of the latest developments in control theory, machine learning, and other relevant fields to effectively tackle the challenges of optimizing dynamic systems. In conclusion, the optimization and control of dynamic systems present multifaceted challenges that require a comprehensive and nuanced approach. From the technical complexities of nonlinear dynamics to the ethical considerations of autonomous systems, the pursuit of optimal control strategies demands a deep understanding of both the theoretical foundations and practical implications. As we navigate this complex landscape, it is essential to foster interdisciplinary collaboration and ethical reflection to ensure that our efforts in optimization and control ultimately serve the common good.。
核电厂数字仪控系统动态可靠性分析方法综述
第41卷第12期2020年12月自动化仪表PROCESS AUT0M\TI0N INSTRl MKNTATIONVol.41 No. 12Dec.2020核电厂数字仪控系统动态可靠性分析方法综述黄晓津,朱云龙,周树桥,郭超(淸屮大学核能与新能源技术研究院,先进反应堆丨:程与安全教部重点实验室,北京丨()()〇84)摘要:仪表~拧制(I&C)系统是核电厂的屮枢神经,对确保核电厂的安全、稳定和经济运行起矜至关®要的作It丨早期使用基于模拟技术的仪控系统对核电厂的状态进行监测和控制,®部件易老化.U维护成本高昂:W此,0前核电厂使用数卞化仪控系统(DCS) 代替模拟仪控系统对于数字化仪控系统软件、硬件耦合以及人因复杂交互等特点,传统的静态可靠性分析方法无法完全适用动态可靠性分析方法可以发现设计中的薄弱环节,改善或增强数字化仪控系统的可靠性总结了动态可靠性分析方法:①当前典型的动态可靠性分折7/法,包括动态失效模式与影响分析(FMEA)、动态故障/事件树(D FT/ET)、动态流图方法(DFM ))、马尔科夫区间映 射方法(Markm/CCMT);②堪于仿K的方法,包括动态决策事忭树(〇[)KT)和连续事件树(CET)方法;③}1;他动态分析方法.包括GO- FLOW、扩展事件序列罔,P etri网该分析为该领域的进一步研究提供参%,关键词:核电厂;数字化仪控系统;动态分析:可靠性;模拟仪控系统;静态可靠性分析中图分类号:TH-86 文献标志码:A D0I: 10. 16086/j. cnki. issn 1000-0380. 2020080019Review of Dynamic Reliability Analysis Methodsfor NPP Digital Instrument and Control SystemHUANG X iao jin,Z H U Y u n lo n g,Z H O U S h u q iao,G U O Chao(Key I^ihoraton of Advanced Reactor Engineering and Safety of Ministn of Education,Institute of Nuclear and N t»w Energy Technology of Tsinghua University, Beijing 100084, China)Abstract :Instrument and control ( l&C) system is the central nerve of nuclear power plants and plays a vital role in ensuring the safety,stability and economic operation of nuclear power plants. In the past,analog I&C system were used to monitor and control the state of nuclear power plants,but the components were prone to aging and high maintenance costs. Therefore,cunently nuclear power plants have used digital I&C systems ( DCS) to substitute analog I&C systems. Traditional static reliahililv analysis methods are not fully qualified,as DCS is rendered by the complex interactions of the software,hardware and human components. Using the dynamic reliability analysis methods, designers can find weaknesses in the DCS design, improve or strengtlien the reliability of these stages. This article summarizes dynamic reliability analysis methods:1the current typical dynamic reliability analysis methods including dynamic failure modes and effect analysis (FM KA) ,dynamic fault/event tree (D F T/E T) ,dynamic flowgraph methodology ( D F M),Markov cell-to-cell mapping technology ( M arkov/CCM T);②simulation-based methods including dynamic decision-event tree ( DDET) and continuous event tree ( C E T) ;(3) other dynamic analysis methods including GO-FLOW, extended event sequence diagram (E SD) ,and Petri net and provide reference for further research in this field.Keywords:Nuclear power plant;Digital instrument and control system;Dynaniic analysis;Reliability;Analog instRiment control system;Static reliability analysis〇引言核电厂具有结构复杂、放射性强的特点,其典型结 构具有两个冋路,运行着许多关键设备(如堆芯、蒸汽 发生器、冷却杲等),一旦设备发生事故,将会对公共 安全、周边环境以及核能产业发展造成巨大的负面影响~。
关于智能交通的专业词汇及缩略词总结
ITS缩略词AADV ANCE(Advance Driver and Vehicle Advisory Navigation Concept)先进的驾驶员咨询与车辆导航概念ADEPT(Automatic Debiting and Electronic Payment for Transport)运输自动借账和电子支付AHS (Automated Highway Systems) 自动公路系统AI(Artificial Intelligence)人工智能ALI(Autofahrer Leit und Information System)驾驶员引导和信息系统AMIS(Advance Mobile Information System)先进的交通信息系统AMTICS(Advanced Mobile Traffic Information and Communication System)先进的汽车交通信息和通信系统APTS(Advanced Public Transportation Systems)先进的公共运输系统ARTIC(Advanced Rural Transportation Information and Coordination)先进的乡村运输信息与协调ARTS(Advanced Rural Transportation Systems)先进的乡村运输系统ARTS(Advanced Road Transportation System)先进的道路交通系统ASV(Advanced Safety Vehicle)先进的安全车辆ATIS(Advanced Traveler Information Systems)先进的旅行者信息系统ATMS(Advanced Traffic Management Systems)先进的交通管理系统ATT(Advanced Transport Telematics)先进的交通通信技术A VCS(Advanced V ehicle Control Systems)先进的车辆控制系统A VCSS(Advanced Vehicle Control and Safety Systems)先进的车辆控制和安全系统A VI (Automatic Vehicle Identification)自动车辆识别A VL(Automatic Vehicle Location)自动车辆定位CCACS(Comprehensive Automobile traffic Control System)汽车交通综合控制系统CCD(Charge-Coupled Device)电荷耦合器件CCTV(Closed Circuit Television)闭路电视CDRG(Centrally-Determined Route Guidance)中心决定的路径诱导CRT(Cathode Ray Tube)阴极射线管CTSCS(Centralized Traffic Signal Control System)中心交通信号控制系统CVISN(Commercial Vehicle information Systems and Networks)商用车辆信息系统与网络CVO(Commercial Vehicle Operation)商用车辆运营DDDN(Digital Data Network)数字数据网DRGS(Dynamic Route Guidance Systems)动态路径诱导系统DRIVE(Dedicated Road Infrastructure for Vehicle Safety in Europe)欧洲道路交通安全设施DRM(Digital Road Map)数字道路地图DRTS(Demand Responsive Public Transportation Services)响应需求的公共运输服务DSRC(Dedicated Shot Range Communication)专用短程通信EEC(European Commission)欧共体,欧洲委员会ECU(Electronic Control Unit)电子控制单元EDI(Electronic Data Interchange)电子数据交换EPMS(Environment Protection Management Systems)环境保护管理系统ERGS(Electronic Route Guidance System)电子路径诱导系统ERTICO(European Road Transport Telematics Implementation Organization)欧洲道路交通通信技术实用化促进组织ETC(Electronic Toll Collection)电子收费ETTM(Electronic Toll and Traffic Management)电子收费和交通管理EUREKA(European Research Coordination Agency)“尤里卡”FFMS(Freeway Management Systems)高速公路管理系统FHWA(Federal Highway Administration)联邦公路管理局GGIS(Geographical Information System)地理信息系统GPS(Global Positioning System)全球定位系统GSM(Global System for Mobile Communications)全球移动通信系统HHELP(Heavy Vehicle Electronic License Plate)重车电子许可牌照HMI(Human-Machime Interface)人机接口HOV(High Occupancy Vehicle)高乘载率车辆HUD(Head Up Display)平面显示器IIC(Integrated Circuit)集成电路IEC(International Electrotechnical Commission)国际电气标准化委员会IMS(Incident Management Systems)事故管理系统ISDN(Integrated Services Digital Network)综合业务数字网ISO(International Organization for Standardization)国际标准化组织ISTEA(Intermodal Surface Transportation Efficiency Act)陆上综合运输效率化法IRVD(infrared vehicle detector)红外车辆检测器ITCS(Integrated Traffic Control Systems)综合交通管制系统ITI(Intelligent Transportation Infrastructure)智能交通运输基础设施ITS(Intelligent Transport Systems)智能运输系统ITS America(Intelligent Transportation Society of America)美国智能运输协会ITTCC(International Telephone and Telegraph Consultative Committee)国际电话与电报顾问委员会ITU(International Telecommunication Union)国际电气通信联合会ITU-R(International Telecommunication Union-Radio Communication Sector)国际电气通信联合会无线通信分委会IVHS(Intelligent Vehicle-Highway System)智能车辆——道路系统JJDRMA(Japan Digital Road Map Association)日本数字道路地图协会JPO(Joint Program Office)美国运输部ITS联合办公室KKAREN(Keystone Architecture Required for European Networks)欧洲运输网络体系结构KoCoRo(Kochi Communication Road)高知通信道路LCD(Liquid Crystal Display)液晶显示LCX(Leakage Coaxial Cable)漏泄同轴电缆LDRG(Locally-Determined Route Guidance)局部决定的路径诱导LED(Light Emitting Diode)发光二极管MMACS(Mainline Automated Clearance System)主线自动放行系统MAGIC(Metropolitan Area Guidance Information and Control)都市圈诱导信息与控制MDT(Mobile Data Terminal)移动数据终端MOCS(Mobile Operation Control Systems)车辆行驶管理系统MEPC(Metropolitan Expressway Public Corporation)都市高速公路公团NNA(National Architecture)(美国)国家(智能运输系统)体系结构NAHSC(National Automated Highway System Consortium)(美国)国家自动公路系统协会PPDP(Plasma Display Panel)等离子体显示屏PROMETHEUS(Program for Europe Traffic with Highest Efficiency and Unprecedented Safety)欧洲高效率和安全交通计划PROMOTE(Program for Mobility in Transportation in Europe)欧洲运输机动性计划PSTN(Public Switch Telephone Network)公众交换电话网PTPS(Public Transportation Priority Systems)公共运输优先系统RACS(Road Automobile Communication System)路、车间通信系统RDS-TMC(Radio Data System-Traffic Message Channel)无线电数据系统——交通消息频道RF(Radio Frequency)射频RM/OSI(Reference Model/Open System Interconnection)开放系统互连参考模型ROMANSE(Road Management System for Europe)欧洲道路管理系统RTT(Road Transport Telematics)道路交通运输信息通讯技术SSATIN(System Architecture And Traffic Control Integration)系统体系结构和交通控制集成SCATS(Sydney Coordinate Condition Adaptable Traffic system)悉尼并行环境自适应交通系统SWIFT(Seattle Wide-Area Information for Travelers)西雅图广域旅行者信息系统SCOOT(Split Cycle Offset Optimization Technique)绿信比-信号周期-绿时差优化技术SSVS(Super Smart Vehicle System)智能汽车系统TTCC(Traffic Control Center)交通控制中心TDC(Travel Dispatch Center)出行调度中心TDM(Transportation Demand Management)运输需求管理TDM(Time Division Multplexing)时分多路复用TEN-T(Trans-European Road Network for Transport)泛欧道路运输网络TERN(Trans-European Road Network)泛欧道路网络TICS(Transportation Information and Control Systems)运输信息和控制系统TravTek(Travel Technology)旅行技术TRB(Transportation Research Board)(美国)运输研究委员会T-TAP(Transport Telemetics Applications Programme)运输通信技术应用计划UUSDOT(United States Department of Transportation)美国运输部UTC(Urban Traffic Control)城市交通控制UTMS(Universal Traffic Management System)新交通管理系统VVERTIS (Vehicle Road Traffic Intelligence Society)道路、交通、车辆智能化推进协会VICS(Vehicle Information and Communication System)新公路交通信息通信系统VMS(Variable Message Signs)可变信息标志WWIM(Weight-In-Motion)动态称重。
会计系统用英语怎么说
会计系统用英语怎么说在单位的内部控制结构中,单位为了记录、分析、汇总、分类、报告单位的业务活动而建立的方法和程序,称之为会计系统。
那么你知道会计系统用英语怎么说吗?下面店铺为大家带来会计系统的英语说法,希望对大家的学习有所帮助!会计系统的英语说法:accounting system会计系统相关英语表达:内部会计系统 Internal accounting system纵横会计系统 Dynamic Accounting System银行会计系统 bank accounting system责任会计系统 Responsibility Accounting System会计系统的英语例句:1. He doesn't know the nuts and bolts of our accounting system.他一点也不了解我们会计系统的基本细节.2. Manual accounting system is used only by small businesses.手工会计系统只适用于小型企业.3. Accounting system is a kind of system of information business control.会计系统既是一种信息系统,更是一种控制系统.4. Most accounting systems and database management systems include an audit trail component.大多数的会计系统和数据库管理系统都包括审计跟踪模块.5. Understand how computerized and manual accounting systems are used.了解计算机会计系统和手工会计系统如何使用.6. Demonstrate the use of double entry and accountingsystems.怎样运用复式计帐法和会计系统.7. Manual accounting systems use special journals to record transactions by category.手工会计系统根据种类使用专门的日记账来记录交易.8. Second, the accounting system separately measures the performance of each responsibility center.第二, 利用会计系统分别测量每个责任中心的绩效.9. Every organization needs an accounting system. Decision makers need information.每个组织都需要一个会计系统, 决策者需要信息.10. To be thoroughly familiar with the accounting computer systems.必须精通电脑会计系统的使用.11. Describe the financial reporting process and how accounting systems control business operations.描述财政报告的过程,并且怎麽会计系统控制经营活动.12. Systems of accounting for manufacturing operations that incorporate perpetual inventories are usually called cost accounting systems.使用永续盘存制的会计系统叫做成本会计系统.13. Secondly, economic and technical changing of enterprise related to digital accounting system, is studied.接着从经济和技术两个方面分析了与数字会计系统相关的企业变化.14. Internal control is a management priority, not merely a part of the accounting system.内部控制首先是一项管理活动, 而不仅仅是会计系统的一个组成部分.15. Updates and maintains the financial information systemwith daily transactions. Ensures that all the accounting system.每日更新和维护中心的财务信息系统以确保所有交易和转账都如期的编入会计系统.。
Control-of-Dynamic-Systems (3)
Control of Dynamic Systems Control of dynamic systems is a crucial aspect of engineering and technology, as it involves the management and regulation of systems that are constantly changing and evolving. This field encompasses a wide range of applications, from industrial processes and manufacturing to aerospace and automotive systems. The ability to effectively control dynamic systems is essential for ensuring safety, efficiency, and reliability in various engineering and technological domains. One of the key challenges in the control of dynamic systems is the inherent complexity and unpredictability of these systems. Dynamic systems are characterized by their continuous and time-varying behavior, making it difficult to accurately model and predict their responses to different inputs and disturbances. This complexity often requires the use of advanced control techniques and algorithms toeffectively manage and regulate dynamic systems in real-time. Another important aspect of controlling dynamic systems is the need to account for uncertainties and disturbances that can affect the system's behavior. These uncertainties can arise from various sources, such as variations in operating conditions, environmental factors, and component failures. As a result, control strategies must be robust and adaptive to ensure that the system can continue to operate safely and effectively under changing and unpredictable conditions. In addition to technical challenges, the control of dynamic systems also involves ethical and social considerations. For example, in the automotive industry, the development of autonomous vehicles raises important questions about safety, liability, and the ethical implications of delegating control to machines. Similarly, in industrial automation, the implementation of advanced control systems can have significant implications for the workforce and employment, raising concerns about job displacement and the ethical use of technology. From a practical standpoint, the control of dynamic systems also requires a multidisciplinary approach, involving expertise in engineering, mathematics, computer science, and other related fields. Engineers and technologists working in this field must be able to collaborate effectively across different disciplines to develop and implement controlsolutions that are both technically sound and practical to deploy in real-world applications. Furthermore, the control of dynamic systems also presentsopportunities for innovation and advancement in engineering and technology. As new control techniques and technologies continue to emerge, there is potential for significant improvements in the performance, efficiency, and safety of dynamic systems across various industries. This ongoing innovation is essential for addressing the evolving needs and challenges of modern society, from sustainable energy systems to advanced transportation solutions. In conclusion, the control of dynamic systems is a complex and multifaceted field that plays a critical role in engineering and technology. From technical challenges and ethical considerations to practical and interdisciplinary requirements, the control of dynamic systems requires a comprehensive and holistic approach. By addressing these various perspectives and challenges, engineers and technologists can continue to advance the state of the art in controlling dynamic systems, leading to safer, more efficient, and more reliable technologies for the benefit of society.。
软件工程英文参考文献(优秀范文105个)
当前,计算机技术与网络技术得到了较快发展,计算机软件工程进入到社会各个领域当中,使很多操作实现了自动化,得到了人们的普遍欢迎,解放了大量的人力.为了适应时代的发展,社会各个领域大力引进计算机软件工程.下面是软件工程英文参考文献105个,供大家参考阅读。
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Computer TechnologyJournal,2020.[10]. Engineering; Study Findings from Beijing Jiaotong University Provide New Insights into Engineering (Analyzing Software Rejuvenation Techniques In a Virtualized System: Service Provider and User Views)[J]. Computer Technology Journal,2020.[11]. Soft Computing; Data on Soft Computing Reported by Researchers at Sakarya University (An exponential jerk system, its fractional-order form with dynamical analysis and engineering application)[J]. Computer Technology Journal,2020.[12]. Engineering; Studies from Henan University Yield New Data on Engineering (Extracting Phrases As Software Features From Overlapping Sentence Clusters In Product Descriptions)[J]. Computer Technology Journal,2020.[13]. Engineering; Data from Nanjing University of Aeronautics and Astronautics Provide New Insights into Engineering (A Systematic Study to Improve the Requirements Engineering Process in the Domain of Global Software Development)[J]. Computer Technology Journal,2020.[14]. Soft Computing; Investigators at Air Force Engineering University Report Findings in Soft Computing (Evidential model for intuitionistic fuzzy multi-attribute group decision making)[J]. Computer Technology Journal,2020.[15]. Engineering; Researchers from COMSATS University Islamabad Describe Findings in Engineering (A Deep CNN Ensemble Framework for Efficient DDoS Attack Detection in Software Defined Networks)[J]. Computer Technology Journal,2020.[16]Pedro Delgado-Pérez,Francisco Chicano. An Experimental and Practical Study on the Equivalent Mutant Connection: An Evolutionary Approach[J]. 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Engineering - Medical and Biological Engineering; Reports from Heriot-Watt University Describe Recent Advances in Medical and Biological Engineering (A Novel Palpation-based Method for Tumor Nodule Quantification In Soft Tissue-computational Framework and Experimental Validation)[J]. Journal of Engineering,2020.[20]. Engineering - Industrial Engineering; Studies from Xi'an Jiaotong University Have Provided New Data on Industrial Engineering (Dc Voltage Control Strategy of Three-terminal Medium-voltage Power Electronic Transformer-based Soft Normally Open Points)[J]. Journal of Engineering,2020.[21]. Engineering; Reports from Hohai University Add New Data to Findings in Engineering (Soft Error Resilience of Deep Residual Networks for Object Recognition)[J]. Journal of Engineering,2020.[22]. Engineering - Mechanical Engineering; Study Data from K.N. 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Gelatin improves peroxidase-mediated alginate hydrogel characteristics as a potential injectable hydrogel for soft tissue engineering applications.[J]. Journal of biomedical materials research. Part B, Applied biomaterials,2020.[30]Jung-Chieh Lee,Chung-Yang Chen. Exploring the team dynamic learning process in software process tailoring performance[J]. Journal of Enterprise Information Management,2020,33(3).[31]. Soft Computing; Study Results from Velammal Engineering College in the Area of Soft Computing Reported (Efficient routing in UASN during the thermohaline environment condition to improve the propagation delay and throughput)[J]. Mathematics Week,2020.[32]. Soft Matter; Findings from School of Materials Science and Engineering Provide New Insights into Soft Matter (A practical guide to active colloids: choosing synthetic model systems for soft matter physics research)[J]. Physics Week,2020.[33]Julio César Puche-Regaliza,Alfredo Jiménez,Pablo Arranz-Val. Diagnosis of Software Projects Based on the Viable System Model[J]. Systemic Practice and Action Research,2020,33(1).[34]Meinert Edward,Milne-Ives Madison,Surodina Svitlana,Lam Ching. Agile requirements engineering and software planning for a digital health platform to engage the effects of isolation caused by social distancing: A case study and feasibility study protocol.[J]. JMIR public health and surveillance,2020.[35]. Engineering - Civil Engineering; Studies Conducted at Shandong Jianzhu University on Civil Engineering Recently Published (Seismic Response Analysis and Control of Frame Structures with Soft First Storey under Near-Fault Ground Motions)[J]. Journal of Engineering,2020.软件工程英文参考文献二:[36]Chao-ze Lu,Guo-sun Zeng,Ying-jie Xie. Bigraph specification of software architecture and evolution analysis in mobile computing environment[J]. Future Generation Computer Systems,2020,108.[37]Ompal Singh, Saurabh Panwar, P. K. Kapur.. Determining SoftwareTime-to-Market and Testing Stop Time when Release Time is a Change-Point[J]. International Journal of Mathematical, Engineering and Management Sciences,2020,5(2).[38]Ayushi Verma,Neetu Sardana,Sangeeta Lal. Developer Recommendation for Stack Exchange Software Engineering Q&A Website based on K-Means clustering and Developer Social Network Metric[J]. Procedia Computer Science,2020,167.[39]Jagdeep Singh,Sachin Bagga,Ranjodh Kaur. Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques[J]. Procedia Computer Science,2020,167.[40]. Engineering - Software Engineering; Studies from Concordia University Update Current Data on Software Engineering (On the impact of using trivial packages: an empirical case study on npm and PyPI)[J]. Computer Technology Journal,2020.[41]. Engineering - Software Engineering; Study Findings from University of Alberta Broaden Understanding of Software Engineering (Building the perfect game - an empirical study of game modifications)[J]. Computer Technology Journal,2020.[42]. Engineering - Software Engineering; Investigators at National Research Council (CNR) Detail Findings in Software Engineering [A Framework for Quantitative Modeling and Analysis of Highly (Re)Configurable Systems][J]. Computer Technology Journal,2020.[43]. Engineering - Knowledge Engineering; Data from University of Paris Saclay Provide New Insights into Knowledge Engineering (Dynamic monitoring of software use with recurrent neural networks)[J]. Computer Technology Journal,2020.[44]. Engineering - Circuits Research; Findings from Federal University Santa Maria Yields New Data on Circuits Research (A New Cpfsk Demodulation Approach for Software Defined Radio)[J]. Computer Technology Journal,2020.[45]. Soft Computing; Investigators from Lovely Professional University Release New Data on Soft Computing (An intensify Harris Hawks optimizer for numerical and engineering optimization problems)[J]. Computer Technology Journal,2020.[46]. GlobalLogic Inc.; GlobalLogic Acquires Meelogic Consulting AG, a European Healthcare and Automotive-Focused Software Engineering Services Firm[J]. Computer Technology Journal,2020.[47]. Engineering - Circuits and Systems Research; Data on Circuits and Systems Research Described by Researchers at Northeastern University (Softcharge: Software Defined Multi-device Wireless Charging Over Large Surfaces)[J]. TelecommunicationsWeekly,2020.[48]. Soft Computing; Researchers from Department of Electrical and Communication Engineering Report on Findings in Soft Computing (Dynamic Histogram Equalization for contrast enhancement for digital images)[J]. Technology News Focus,2020.[49]Mohamed Ellithey Barghoth,Akram Salah,Manal A. Ismail. A Comprehensive Software Project Management Framework[J]. Journal of Computer and Communications,2020,08(03).[50]. Soft Computing; Researchers from Air Force Engineering University Describe Findings in Soft Computing (Random orthocenter strategy in interior search algorithm and its engineering application)[J]. Journal of Mathematics,2020.[51]. Soft Computing; Study Findings on Soft Computing Are Outlined in Reports from Department of Mechanical Engineering (Constrained design optimization of selected mechanical system components using Rao algorithms)[J]. Mathematics Week,2020.[52]Iqbal Javed,Ahmad Rodina B,Khan Muzafar,Fazal-E-Amin,Alyahya Sultan,Nizam Nasir Mohd Hairul,Akhunzada Adnan,Shoaib Muhammad. Requirements engineering issues causing software development outsourcing failure.[J]. PloS one,2020,15(4).[53]Raymond C.Z. Cohen,Simon M. Harrison,Paul W. Cleary. Dive Mechanic: Bringing 3D virtual experimentation using biomechanical modelling to elite level diving with the Workspace workflow engine[J]. Mathematics and Computers in Simulation,2020,175.[54]Emelie Engstr?m,Margaret-Anne Storey,Per Runeson,Martin H?st,Maria Teresa Baldassarre. How software engineering research aligns with design science: a review[J]. Empirical Software Engineering,2020(prepublish).[55]Christian Lettner,Michael Moser,Josef Pichler. An integrated approach for power transformer modeling and manufacturing[J]. Procedia Manufacturing,2020,42.[56]. Engineering - Mechanical Engineering; New Findings from Leibniz University Hannover Update Understanding of Mechanical Engineering (A finite element for soft tissue deformation based on the absolute nodal coordinate formulation)[J]. Computer Technology Journal,2020.[57]. Science - Social Science; Studies from University of Burgos Yield New Information about Social Science (Diagnosis of Software Projects Based on the Viable System Model)[J]. Computer Technology Journal,2020.[58]. Technology - Powder Technology; Investigators at Research Center Pharmaceutical Engineering GmbH Discuss Findings in Powder Technology [Extended Validation and Verification of Xps/avl-fire (Tm), a Computational Cfd-dem Software Platform][J]. Computer Technology Journal,2020.[59]Guadalupe-Isaura Trujillo-Tzanahua,Ulises Juárez-Martínez,Alberto-Alfonso Aguilar-Lasserre,María-Karen Cortés-Verdín,Catherine Azzaro-Pantel. Multiple software product lines to configure applications of internet of things[J]. IET Software,2020,14(2).[60]Eduardo Juárez,Rocio Aldeco-Pérez,Jose.Manuel Velázquez. Academic approach to transform organisations: one engineer at a time[J]. IET Software,2020,14(2).[61]Dennys García-López,Marco Segura-Morales,Edson Loza-Aguirre. Improving the quality and quantity of functional and non-functional requirements obtained during requirements elicitation stage for the development of e-commerce mobile applications: an alternative reference process model[J]. IET Software,2020,14(2).[62]. Guest Editorial: Software Engineering Applications to Solve Organisations Issues[J]. IET Software,2020,14(2).[63]?,?. Engine Control Unit ? ? ?[J]. ,2020,47(4).[64]. Engineering - Software Engineering; Study Data from Nanjing University Update Understanding of Software Engineering (Identifying Failure-causing Schemas In the Presence of Multiple Faults)[J]. Mathematics Week,2020.[65]. Energy - Renewable Energy; Researchers from Institute of Electrical Engineering Detail New Studies and Findings in the Area of Renewable Energy (A Local Control Strategy for Distributed Energy Fluctuation Suppression Based on Soft Open Point)[J]. Journal of Mathematics,2020.[66]Ahmed Zeraoui,Mahfoud Benzerzour,Walid Maherzi,Raid Mansi,Nor-Edine Abriak. New software for the optimization of the formulation and the treatment of dredged sediments for utilization in civil engineering[J]. Journal of Soils and Sediments,2020(prepublish).[67]. Engineering - Concurrent Engineering; Reports from Delhi Technological University Add New Data to Findings in Concurrent Engineering (Systematic literature review of sentiment analysis on Twitter using soft computing techniques)[J]. Journal of Engineering,2020.[68]. Engineering; New Findings from Future University in Egypt in the Area of Engineering Reported (Decision support system for optimum soft clay improvementtechnique for highway construction projects)[J]. Journal of Engineering,2020.[69]Erica Mour?o,Jo?o Felipe Pimentel,Leonardo Murta,Marcos Kalinowski,Emilia Mendes,Claes Wohlin. On the performance of hybrid search strategies for systematic literature reviews in software engineering[J]. Information and Software Technology,2020,123.[70]. Soft Computing; Researchers from Anna University Discuss Findings in Soft Computing (A novel fuzzy mechanism for risk assessment in software projects)[J]. News of Science,2020.软件工程英文参考文献三:[71]. Software and Systems Research; New Software and Systems Research Study Results from Chalmers University of Technology Described (Why and How To Balance Alignment and Diversity of Requirements Engineering Practices In Automotive)[J]. Journal of Transportation,2020.[72]Anupama Kaushik,Devendra Kr. Tayal,Kalpana Yadav. A Comparative Analysis on Effort Estimation for Agile and Non-agile Software Projects Using DBN-ALO[J]. Arabian Journal for Science and Engineering,2020,45(6).[73]Subhrata Das,Adarsh Anand,Mohini Agarwal,Mangey Ram. Release Time Problem Incorporating the Effect of Imperfect Debugging and Fault Generation: An Analysis for Multi-Upgraded Software System[J]. International Journal of Reliability, Quality and Safety Engineering,2020,27(02).[74]Saerom Lee,Hyunmi Baek,Sehwan Oh. The role of openness in open collaboration:A focus on open‐source software development projects[J]. ETRI Journal,2020,42(2).[75]. Soft Computing; Study Results from Computer Science and Engineering Broaden Understanding of Soft Computing (Efficient attribute selection technique for leukaemia prediction using microarray gene data)[J]. Computer Technology Journal,2020.[76]. Engineering - Computational Engineering; Findings from University of Cincinnati in the Area of Computational Engineering Described (Exploratory Metamorphic Testing for Scientific Software)[J]. Computer Technology Journal,2020.[77]. Organizational and End User Computing; Data from Gyeongnam National University of Science and Technology Advance Knowledge in Organizational and End User Computing (A Contingent Approach to Facilitating Conflict Resolution in Software Development Outsourcing Projects)[J]. Computer Technology Journal,2020.[78]. Soft Computing; Findings from Department of Industrial Engineering in the Area of Soft Computing Reported (Analysis of fuzzy supply chain performance based on different buyback contract configurations)[J]. Computer Technology Journal,2020.[79]Hana Mkaouar,Bechir Zalila,Jér?me Hugues,Mohamed Jmaiel. A formal approach to AADL model-based software engineering[J]. International Journal on Software Tools for Technology Transfer,2020,22(5).[80]Riesch Michael,Nguyen Tien Dat,Jirauschek Christian. bertha: Project skeleton for scientific software.[J]. PloS one,2020,15(3).[81]. Computers; Findings from Department of Computer Sciences and Engineering Reveals New Findings on Computers (An assessment of software defined networking approach in surveillance using sparse optimization algorithm)[J]. Telecommunications Weekly,2020.[82]Luigi Ranghetti,Mirco Boschetti,Francesco Nutini,Lorenzo Busetto. “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data[J]. 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Development of computational models of emotions: A software engineering perspective[J]. Cognitive Systems Research,2020,60(C).[99]Sharifzadeh Bahador,Kalbasi Rasool,Jahangiri Mehdi,Toghraie Davood,Karimipour Arash. Computer modeling of pulsatile blood flow in elastic artery using a software program for application in biomedical engineering[J]. Computer Methods and Programs in Biomedicine,2020.[100]Shen Xiaoning,Guo Yinan,Li Aimin. Cooperative coevolution with an improved resource allocation for large-scale multi-objective software project scheduling[J]. Applied Soft Computing,2020,88(C).[101]Jung Jaesoon,Kook Junghwan,Goo Seongyeol,Wang Semyung. Corrigendum to Sound transmission analysis of plate structures using the finite element method and elementary radiator approach with radiator error index [Advances in Engineering Software 112 (2017 115][J]. Advances in Engineering Software,2020,140(C).[102]Zhang Chenyi,Pang Jun. 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Introductions to ControlSystems
Automatic control has played a vital role in the advancement of engineering and science. In addition to its extreme importance in space-vehicle, missile-guidance, and aircraft-piloting systems, automatic control has become an important and integral part of modern manufacturing and industrial processes.自动控制在工程和科学领域起着很重要的作用。
除了在宇宙飞船、导弹的制导和飞机驾驶系统中起重要作用外,自动控制已经成为现代生产及工业过程中重要而不可缺少的组成部分。
Since advances in the theory and practice of automatic control provide means for attaining optimal performance of dynamic systems, improve the quality and lower the cost of production, expand the production rate, relieve the drudgery of many routine, repetitive manual operations, most engineers and scientists must now have a good understanding of this field. 由于自动控制理论和实践的不断发展给人们提供了获得动态系统最佳特性的方法,提高了产品质量,降低了生产成本,提高了生产率,使人们从繁重的日常工作和重复的手工操作中解放出来。
SIMADYN D数字控制系统处理器模块PM16用户手册说明书
SIMADYN DUser Manual Digital Control SystemProcessor module PM16Edition 05.95DK-Nr. 221441User Manual, Processor module PM16Edition Edition status 1Processor module PM1603.91 2Processor module PM1605.95Copying of this document and giving it to others and the use orcommunication of the contents thereof is forbidden without expressauthority. Offenders are liable to the payment of damages. All rightsare reserved in the event of the grant of a patent or the registration ofa utility model or design.We have checked the contents of this Manual to ensure that theycoincide with the described hardware and software. However,deviations cannot be completely ruled-out, so we cannot guaranteecomplete conformance. However, the information in this document isregularly checked and the necessary corrections included insubsequent editions. We are thankful for any recommendations orsuggestions.ContentsContentsWarning information (1)1. Order Designation: (3)2. Functional Description (3)3. Board Design (4)4. Application Notes (5)5. Technical Specifications (8)5.1. General Data (8)5.2. Electrical data (8)5.2.1. Power supply (8)5.2.2. Binary inputs (9)5.2.3. Binary outputs (9)5.2.4. Serial Interfaces (9)6. Pin allocation of the PM16 (10)6.1. Allocation of the serial interfaces X01, X02 (10)6.2. Pin allocation of the binary inputs and outputs, Connector X5 (11)7. STRUC L-Mask for the PM16 board Master program (12)8. Appendix (13)8.1. Block diagram (13)8.2. Scale drawing and connector table (13)8.3. Arrangement drawing (13)9. Miscellaneous (13)10. ECB instructions (14)Siemens AG Dk-Nr. 221441Edition 05.95SIMADYN D Hardware User ManualWarning informationEdition 05.95Siemens AG Dk-Nr. 221441SIMADYN D Hardware User ManualWarning informationN O T E !The information in this Manual does not purport to cover all details or variations in equipment, nor to provide for every possible contingency to be met in connection with installation, operation or maintenance.Should further information be desired or should particular problems arise which are not covered sufficiently for the purchaser’s purposes, please contact your local Siemens office.Further, the contents of this Manual shall not become a part of or modify any prior or existing agreement, committment or relationship. The sales contract contains the entire obligation of Siemens. The warranty contained in the contract between the parties is the sole warranty of Siemens. Any statements contained herein do not create new warranties nor modify the existing warranty.Warning informationSiemens AG Dk-Nr. 221441Edition 05.951 SIMADYN D Hardware User ManualWarning information2Edition 05.95Siemens AG Dk-Nr. 221441SIMADYN D Hardware User ManualDefinitions*QUALIFIED PERSONNELFor the purpose of this User Manual and product labels, a …Qualified person“ is someone who is familiar with the installation, mounting, start-up and operation of the equipment and the hazards involved. He or she must have the following qualifications:1.Trained and authorized to energize, de-energize, clear, ground and tag circuits and equipment in accordance with established safety procedures.2.Trained in the proper care and use of protective equipment in accordance with established safety procedures.3.Trained in rendering first aid.*DANGERFor the purpose of this User Manual and product labels, …Danger“ indicates death, severe personal injury and/or substantial property damage will result if proper precautions are not taken.*WARNINGFor the purpose of this User Manual and product labels, …Warning“ indicates death, severe personal injury or property damage can result if proper precautions are not taken.*CAUTIONFor the purpose of this User Manual and product labels, …Caution“ indicates that minor personal injury or material damage can result if proper precautions are not taken.*NOTEFor the purpose of this User Manual, …Note“ indicates information about the product or the respective part of the User Manual which is essential to highlight.W A R N I N G !Hazardous voltages are present in this electrical equipment during operation.Non-observance of the safety instructions can result in severe personal injury orproperty damage.It is especially important that the warning information in all of the relevantOperating Instructions are strictly observed.Order Designation: 1. Order Designation:6DD 1600 - 0AF0 Processor module PM16 with 128K RAM for software versions from 3.0 .2. Functional DescriptionThe processor module PM16 processes general technological control, calculation and regulation tasks in the SIMADYN D system. These tasks lie above the drive level control and regulation functions (torque shell). The board contains the -CMOS- 16 bit microprocessor 80C186 - 16MHz with corresponding peripherals.Plug-in program memory modules (MS31, MS3) are used in the mounting location X50 for the board user programs as well as for the system firmware (operating system, supervisor program, function module code, .... ). The user programs run on the processor under the SIMADYN D real time operating system. This guarantees interrupt controlled fixed cycle times of ≥ 1ms, dependent upon the configuration.There are 16 binary input and 16 binary output channels available for the fast exchange of data with the process IO (connector X5).The binary inputs can be declared, via software, as interrupt inputs. At the occurrence of a signal edge at an interrupt input, the processor interrupts the current cyclic processing and runs the function packet process interrupt job PIJ . Connection cables carrying binary signals are connected to interface modules and not directly to the processor module.The interface modules implement both the mechanical connection terminal and the electrical signal adaptation. The plant signals can be directly connected to these terminals.Two serial interfaces (connectors X01, X02) are available for communication :- to a higher level computer- for data transfers between SIMADYN D systems- listing outputs to printers- to the SIMADYN D system peripheral IO(operator panel OP1, service unit US1 and programming unit PG 675, 685 or 750)The seven segment display on the board front panel, indicates a "-" character during the start-up phase and the configured processor number during normal operation. The display flashes with an error code when a fault occurs.The error codes are described in the processor module handling instructions /1/.When an error message is displayed, the HEX supervisor can be activated by pressing the S1 key.A forced board reset (Restart) can be initiated using the twin jack connectors X10 and X11 . The jack connectors must therefore be jumpered by a switch or a shorting plug.The 50 pin diagnostic connector X4 is available on the board for hardware diagnostics using a logic analyzer or a recorder.Three watchdogs are installed on each processor board to monitor the hardware and software system states.Siemens AG Dk-Nr. 221441Edition 05.953 SIMADYN D Hardware User ManualBoard DesignThe hardware monitor checks:- Ready signal time-outs during system bus accessing- Double address decoding errors- Accessing unused or non-existent addresses- Collision detection of a DMA access with a system bus access (Detection can be disabled bysoftware)- System bus fault messagesThe software monitor checks:- Whether the processor is still running a cyclic task.- Whether the interrupt controller for the serial interface, timer and inputs are fully operational.A "Non-Maskable Interrupt" (NMI) is generated when the supervisor detects a fault. The processor attempts to resolve the problem and resume cyclic operation. If the fault is caused by the processor itself, then the processor switches to 'inactive', the red dot on the seven segment display is switched on and the bus signal "system error" is activated.3. Board Design- Connectors for local and communication busses.- CPU 80C186 - 16 MHz- RAM 128 K ByteBattery buffered by the power supply (PS)- Connector terminal for the program memory sub-modules MS3/31/4/45- 2 serial interfacesselection of V24(RS232), 20mA(TTY), RS485- 16 binary inputsno galvanic isolation, used as interrupt controlled inputs- 16 binary outputsno galvanic isolationmaximum of 30V / 50mA- Real time clockresolution 10 ms; battery buffered by the PS- 7 segment display for the configured processor numberor error display- Board identification4Edition 05.95Siemens AG Dk-Nr. 221441SIMADYN D Hardware User ManualApplication Notes- Hardware and software monitoring by watchdogs- Test connector for a logic analyzer or recorder4. Application NotesThe processor module PM16 can be installed in both the large racks such as SR1 and SR5 with local and communication busses and the small racks such as SR2 and SR4 with local bus. It occupies two standard slots in the racks.The rack must either be installed with the bus terminator or a memory coupling board.The board can be installed on any rack slot with "slot number coding", that contains the SIMADYN D system bus interface. Whereby, it should be noted that the left-aligned slot must be installed with a local bus master (processor module). If this is not adhered to, then the local peripheral boards will not be supplied with the 8MHz clock. Daisy chain jumpers must be installed on empty slots for multi-processor configurations.The board must be fixed to the rack by screws (even during commissioning) to ensure correct functioning.If the board is connected to an adapter, then the frame must be shorted to the rack housing by a short conductor.The board may not be pulled or installed under power.When the serial interfaces X01 and X02 are used, then thick film interface modules (hybrid modules) must be installed. The following hybrid modules are currently available:SS1 : 20 mA (TTY)SS2 : V.24 (RS 232)SS3 : (RS 485)The hybrid module for the serial interface X01 is to be installed on connector X51 (U1) and on connector X52 (U2, see printed diagram) for the interface X02 .ATTENTION: CHECK INSTALLATION LOCATION CAREFULLY!The binary inputs and outputs are connected via interface modules, which are fixed to a terminal rail. The connection from the board connector X5 to the interface modules is implemented with ribbon cable.Siemens AG Dk-Nr. 221441Edition 05.955 SIMADYN D Hardware User ManualApplication Notes6Edition 05.95Siemens AG Dk-Nr. 221441SIMADYN D Hardware User Manual The following connection configurations are possible:a) All 16 binary inputs and outputs are brought from the PM16 connector X5 to the interface module SE3.1 (24V no galvanic isolation) via a 40 pin ribbon cable.The external connection to the plant are implemented there (screw terminals).X516 Binärin/outputs 24V PM16ribbon cable 40-wayb) The 16 binary inputs and outputs are distributed to 4 different interface modules. A ribbon cable istherefore connected to the PM16 board connector X5 with split connectors at the other end (4x10 pin) which are then brought out to the interface modules. It is then possible to connect e.g. 8 inputs or outputs with galvanic isolation and 8 without galvanic isolation. The reference voltage ofM24/screen may be selected via the DIP-FIX switch S2 for the binary input signals. The default setting is the reference voltage for M24 (s. 2GE 465 600 9005.01 AO).X516 binaryin/outputs 24V PM16binary signals 40-way10-way 10-way 10-way 10-wayApplication NotesAdditional PM16 components:a) Serial Interfaces- Hybrid interface SS1 (20 mA)6DD 1688-1AA0- Hybrid interface SS2 ( V24 )6DD 1688-1AB0- Cable PM-SE12.1 : SC30.1 20 mA/ 2 m6DD 1684-0DA1- Cable PM-AS 512 : SC22.1 20 mA/ 10 m6DD 1684-0CC1- Cable PM-PG 675 : SC32 20 mA/ 10 m6DD 1684-0DC0- Cable PM-printer : SC34 20 mA/ 10 m6DD 1684-0DE0- Set of parts 25pin Cannon connector: SM3.16DD 1680-0AD0- Hybrid interface SS3 (RS485)6DD 1688-1AC0- Cable : SC27 RS485/ 2,1 m6DD 1684-0CH0- SE 47.1 Bus connector module6DD 1681-0EH0b) Binary input cable- 40pin 2,0 m SC186DD 1684-0BJ0- 40pin --> 4+10pin 2,0 m SC136DD 1684-0BD0c) Interface module- SE3.16DD 1681-0AD016 Binary inputs and outputs, no galvanic isolation- SE4.16DD 1681-0AE18 Binary inputs and outputs, no galvanic isolation- SE5.36DD 1681-0AF38 Binary inputs maximum 220V galvanic isolation- SE6.16DD 1681-0AG18 Binary outputs maximum 220V galvanic isolation- SE376DD 1681-0DH08 Binary outputs 24V galvanic isolation- SE41.16DD 1681-0EB18 Binary inputs 48V galvanic isolation6DD 1681-0EB28 Binary inputs 24V galvanic isolationTechnical Specifications5. Technical Specifications5.1. General DataINSULATION GROUP A FROM VDE 011 PARAGRAPH 13 GROUP 2 AT 24V-,15V,5V-AMBIENT TEMPERATURE0 TO 55 DEG. C WITH FORCED VENTILATIONSTORAGE TEMPERATURE-40 TO +70 DEG. CHUMIDITY CLASS F ACCORDING TO DIN 40050ALTITUDE RATING S ACCORDING TO DIN 40040MECHANICAL STRESS INSTALL IN FIXED EQUIPMENT, SENSITIVE TO VIBRATIONS PACKAGING SYSTEM ES 902 CDIMENSIONS233,4 * 220 MMBOARD WIDTH 2 2/3 SEP = 2EB = 40.28 MMWEIGHT0,7 KG5.2. Electrical data5.2.1. Power supplycross-connectionprotection Fuse protectionVOLTAGE+ 5 V no noVOLTAGE+ 15 V no noVOLTAGE- 15 V no noVOLTAGE VCC no noVOLTAGE+ 3,4 V EXT yes noDES MIN TYPICAL MAX UNIT VOLTAGE(+ 5 V)+ 5 V+ 4,75+ 5,25V VOLTAGE(+ 15 V)+ 15 V+ 14,40+ 15,60V VOLTAGE(- 15 V)- 15 V- 14,40- 15,60V VOLTAGE VCC VCC+ 2,20+ 5,25V VOLTAGE(+ 3,4 V EXT)+ 3,4 V EXT+ 2,20+ 5,90V HARMONICS(+ 5 V)0,10Vpp HARMONICS(+ 15 V)0,15Vpp HARMONICS(- 15 V)0,15Vpp HARMONICS VCC ---Vpp HARMONICS(+ 3,4 V) ---Vpp CURRENT(+ 5 V) without modules1,30A CURRENT(+ 5 V) without module1,40A CURRENT(+ 15 V)0,05A CURRENT(- 15 V)0,05A CURRENT VCC (buffered)0,80mA CURRENT(+ 3,4 V) (buffered)1,15mA POWER LOSS(+ 5 V) without modules7,00VAPOWER LOSS(+ 15 V)0,78VAPOWER LOSS(- 15 V)0,78VAVCC4,00mVA(+ 3,4 V)3,90mVA8Edition 05.95Siemens AG Dk-Nr. 221441Technical Specifications 5.2.2. Binary inputsNUMBER16 NO GALVANIC ISOLATIONINPUT VOLTAGE+ 24 V RATED VALUEINPUT VOLTAGEFOR 0 SIGNAL-1 V TO + 6 V ;OR BINARY INPUT OPENFOR 1 SIGNAL+ 13 V TO + 33VINPUT CURRENTFOR 1 SIGNAL TYP. 3 MARESPONSE TIME220 uS with hybrid capacitorRESPONSE TIME20 uS without hybrid capacitor / Standard design5.2.3. Binary outputsNUMBER16 NO GALVANIC ISOLATION!POWER SUPPLY P24 EXTERNAL SUPPLY-RATED VALUE24 V --HARMONICS 3.6 V --PERM. RANGE+ 20 TO +30 V INCL. HARMONICS-TEMPORARILY+ 35 V SMALLER 0,5 SEC.CURRENT COMSUMP.P24 MAX. 900mAOUTPUT CURRENT FOR 1 SIGNAL-RATED VALUE50 MA-PERM. RANGE0.2 MA TO 50 MASHORT CIRCUIT PROTECT ELECTRONICINDUCTIVE LIMITATIONTRIP VOLTAGE AT V CC + 1 VTOTAL LOADING80 % FOR 50 DEG C ALL OUTPUTS 50 MARESIDUAL CURRENT20 uA FOR O SIGNALSIGNAL LEVEL-FOR 0 SIGNAL MAX. 3 V-FOR 1 SIGNAL MIN. SUPPLY - 2.5 VSWITCHING DELAY15 uS5.2.4. Serial InterfacesNUMBER2DATA RATE MAX. 19.2 KBd / SS1 (20 mA) / SS2 (V24)MAX. 1.0 MBD / SS3 (RS485)Pin allocation of the PM166. Pin allocation of the PM166.1. Allocation of the serial interfaces X01, X02PIN V2420 MA (TTY)1FRAME GROUND FRAME GROUND2TRANSMIT DATA OUT T*D ---3RECEIVE DATA IN R*D ---4REQUEST TO SEND OUT*RTS ---5CLEAR TO SEND*CTS ---6DATA SET READY IN ---7GROUND ---8DATA CARRIER DETECT IN*DCD ---9GROUND GROUND10 ---CURRENT LOOP + TRANSMIT+T*D11+ 15 V+ 15 V12 ---20 MA SOURCE 113 ---CURRENT LOOP + RECEIVE+R*D14 ---CURRENT LOOP - RECEIVE-R*D15RECEIVE/TRANSMIT CLOCK*RT*C ---16 ---20 MA SOURCE 217RECEIVE/TRANSMIT CLOCK ---18GROUND GROUND19 ---CURRENT LOOP - TRANSMIT-T*D20DATA TERMINAL READY OUT ---21 ---20 MA DRAIN 122+ 5 V+ 5 V23+ 5 V+ 5 V24TRANSMIT RECEIVE CLOCK*TR*C20 MA DRAIN 225- 15 V- 15 VPIN RS4851FRAME GROUND2REQUEST TO SEND+OUT 1,D3TRANSMIT/RECEIVE CLOCK+IN 2,R4 ---5TRANSMIT DATA OUT+OUT 2,D6RECEIVE/TRANSMIT CLOCK+IN 3,R7DATA CARRIER DETECT+IN 4,R8RECEIVE DATA IN+IN 1,R9GROUND10 ---11+ 15 V12 ---13 ---14REQUEST TO SEND-OUT 1,D15TRANSMIT/RECEIVE CLOCK-IN 2,R16 ---17TRANSMIT DATA OUT-OUT 2,D18GROUND19RECEIVE/TRANSMIT CLOCK-IN 3,R20DATA CARRIER DETECT-IN 4,R21RECEIVE DATA IN-IN 1,R22+ 5 V23+ 5 V24 ---25- 15 V10Edition 05.95Siemens AG Dk-Nr. 221441Pin allocation of the PM166.2. Pin allocation of the binary inputs and outputs, Connector X5Ribbon cable connectorPIN DES.CONNECTOR1OUTPUT 1X5 A2OUTPUT 2X5 A3OUTPUT 3X5 A4OUTPUT 4X5 A5OUTPUT 5X5 A6OUTPUT 6X5 A7OUTPUT 7X5 A8OUTPUT 8X5 A9P EXTERNAL X5 A10M EXTERNAL X5 A11OUTPUT 9X5 B12OUTPUT 10X5 B13OUTPUT 11X5 B14OUTPUT 12X5 B15OUTPUT 13X5 B16OUTPUT 14X5 B17OUTPUT 15X5 B18OUTPUT 16X5 B19P EXTERNAL X5 B20M EXTERNAL X5 B21INPUT 1X5 C pos. INTERRUPT CONTROLLED22INPUT 2X5 C pos. INTERRUPT CONTROLLED23INPUT 3X5 C pos. INTERRUPT CONTROLLED24INPUT 4X5 C pos. INTERRUPT CONTROLLED25INPUT 5X5 C pos. INTERRUPT CONTROLLED26INPUT 6X5 C pos. INTERRUPT CONTROLLED27INPUT 7X5 C pos. INTERRUPT CONTROLLED28INPUT 8X5 C pos. INTERRUPT CONTROLLED29P EXTERNAL30M EXTERNAL31INPUT 9X5 D pos. INTERRUPT CONTROLLED32INPUT 10X5 D pos. INTERRUPT CONTROLLED33INPUT 11X5 D pos. INTERRUPT CONTROLLED34INPUT 12X5 D pos. INTERRUPT CONTROLLED35INPUT 13X5 D pos. INTERRUPT CONTROLLED36INPUT 14X5 D pos. INTERRUPT CONTROLLED37INPUT 15X5 D pos. INTERRUPT CONTROLLED38INPUT 16X5 D pos. INTERRUPT CONTROLLED39P EXTERNAL40M EXTERNALSTRUC L-Mask for the PM16 board Master program7. STRUC L-Mask for the PM16 board Master program(see Master program description)STRUC-L MASK: PM16 ^"processor module 1 standard, L+C-bus^"PIJ 1N = 0 ^"alarm processing FP^"SFJ 1N = 0 ^"system error FP^"PRX 1N = 0 ^"special communication FP receive^"PJ1 1N = ? ^"1. permanent processing FP^"PJ2 1N = 0PJ3 1N = 0PJ4 1N = 0PJ5 1N = 0PJ6 1N = 0PJ7 1N = 0PJ8 1N = 0PTX 1N = 0 ^"special communication FP transmit^"ILS IK = 0 ^"L-Bus-Interrupt transmit^"ICS IK = 0 ^"C-Bus-Interrupt transmit^"TO TG = ? ^"basic sampling time^"T1 TS = ? ^"1. s.t.*T0,produced LB- and CB-conn.^"T2 TS = ? ^"2. s.t. ^" ^"T3 TS = ? ^"3. s.t. ^" ^"T4 TS = ? ^"4. s.t. ^" ^"T5 TS = ? ^"5. s.t. ^" ^"TY TX = T? ^"sampling time of system FP^"SSM 2C = 0 ^"Length SAVE-area, (n*1+2) kByte^"ISE 1C = N ^"Ignore syst. except. (RDYINT) (Y/N) ?^"CCT 8R = 0 ^"transmitter communication names.Tx^"CCR 8R = 0 ^"receiver communication names.Tx^"COP 8R = 0 ^"service communication names.Tx^"CMS 8N = 0 ^"message system names^"CTS 8N = 0 ^"comm. transport system names^"MS 2M = 0 ^"message systems^"X01 1M = 0 ^"1. serial interface^"X02 1M = 0 ^"2. serial interface^"X5C 8K < ^"binary inp. 1, interrupt ctr.^"X5D 8K < ^"binary inp. 2, interrupt ctr.^"X5A 8K > ^"binary outputs 1^"X5B 8K > ^"binary outputs 2^"The PM16 requires 3 Sub-modules :- 1 * PROGRAM MEMORY- 2 * SERIAL INTERFACESThe X5 connector, binary input and output, can be accessed by the following function modules: CONN. SECTION FUNCTION MODULEX5C -|--|- BII8 Binary input (8 Binary valu&es)X5D -| |- BID8 Binary input (8 Binary valu&es, normal mode)12Edition 05.95Siemens AG Dk-Nr. 221441Appendix|- SBI Numerical input, ByteX5A -|--|- BIQ8 Binary output (8 Binary valu&es)X5B -| |- BQD8 Binary output (8 Binary valu&es, normal mode)|- SBQ Numerical output, Byte8. Appendix8.1. Block diagramBlock diagram 3GE.465 600.9005.01 SU8.2. Scale drawing and connector tableScale drawing with front panel view and table ofthe utilized connectors 3GE.465 600.9005.00 MB8.3. Arrangement drawingArrangement drawing 3GE.465 600.9005.02 AO9. MiscellaneousECB instructions10. ECB instructionsComponents which can be destroyed by electrostatic discharge (ECB)Generally, electronic boards should only be touched when absolutely necessary.The human body must be electrically discharged before touching an electronic board. This can be simply done by touching a conductive, grounded object directly beforehand (e.g. bare metal cubicle components, socket outlet protective conductor contact.Boards must not come into contact with highly-insulating materials - e.g. plastic foils, insulated desktops, articles of clothing manufactured from man-made fibers.Boards must only be placed on conductive surfaces.When soldering, the soldering iron tip must be grounded.Boards and components should only be stored and transported in conductive packaging (e.g. metalized plastic boxes, metal containers).If the packing material is not conductive, the boards must be wrapped with a conductive packing material, e.g. conductive foam rubber or household aluminum foil.The necessary ECB protective measures are clearly shown in the following diagram.a = Conductive floor surface d = ECB overallb = ECB table e = ECB chainc = ECB shoes f = Cubicle ground connection14Edition 05.95Siemens AG Dk-Nr. 221441ECB instructionsECB instructions16Edition 05.95Siemens AG Dk-Nr. 221441Drives and Standard Products Motors and Drives Systems GroupPostfach 3269, D-91050 ErlangenSystem-Based Technology。
Advanced Control Theory and Applications
Advanced Control Theory and Applications Advanced control theory and applications are an essential part of modern engineering and technology. It encompasses a wide range of techniques and methodologies that are used to design and implement control systems for various applications, such as robotics, aerospace, automotive, and industrial automation. The field of control theory has seen significant advancements in recent years,with the development of new algorithms, methods, and tools that haverevolutionized the way control systems are designed and implemented. One of the key challenges in advanced control theory and applications is the need to develop control systems that are robust, reliable, and efficient. This requires a deep understanding of the underlying dynamics of the system being controlled, as wellas the ability to design control algorithms that can effectively deal with uncertainties, disturbances, and variations in the system. Advanced control techniques such as model predictive control, adaptive control, and nonlinearcontrol have been developed to address these challenges, and they have been successfully applied to a wide range of real-world systems. Another important aspect of advanced control theory and applications is the integration of control systems with other technologies, such as artificial intelligence, machine learning, and data analytics. This integration allows for the development of intelligent control systems that can learn from data, adapt to changing conditions, and optimize their performance over time. This has led to the development of advanced control systems for autonomous vehicles, smart grids, and industrial processes, among others. In addition to the technical challenges, there are also practical considerations that need to be taken into account when applying advanced control theory to real-world systems. These include issues such as cost, safety, and regulatory compliance, which can have a significant impact on the design and implementation of control systems. For example, in the automotive industry, advanced control systems need to meet stringent safety standards and regulatory requirements, while also being cost-effective and reliable. From a research perspective, advanced control theory and applications present a wide range of exciting opportunities for further exploration and development. There are still many open problems and unanswered questions in the field, and researchers areconstantly working on new approaches and methodologies to address these challenges. This includes the development of new control algorithms, the integration ofcontrol systems with emerging technologies, and the application of advancedcontrol techniques to new and emerging application areas. In conclusion, advanced control theory and applications play a crucial role in modern engineering and technology, and they have the potential to revolutionize the way we design and implement control systems for a wide range of applications. The field presents a number of technical and practical challenges, as well as exciting opportunitiesfor further research and development. By addressing these challenges and opportunities, researchers and engineers can continue to advance the state of the art in control theory and applications, leading to the development of more robust, reliable, and efficient control systems for the future.。
离散控制 连续控制 脉冲控制
离散控制连续控制脉冲控制1.离散控制是指系统的输入和输出是在一系列离散时间点上进行调整的。
Discrete control refers to the adjustment of the system's input and output at a series of discrete time points.2.连续控制是指系统的输入和输出是在连续的时间上进行调整的。
Continuous control refers to the adjustment of thesystem's input and output at continuous time.3.脉冲控制是指系统的输入和输出是通过脉冲信号进行调整的。
Pulse control refers to the adjustment of the system's input and output through pulse signals.4.离散控制通常用于数字系统或者离散事件系统。
Discrete control is commonly used in digital systems or discrete event systems.5.连续控制通常用于模拟系统或者连续事件系统。
Continuous control is commonly used in analog systems or continuous event systems.6.脉冲控制通常用于需要精确定时和控制的系统中。
Pulse control is commonly used in systems that require precise timing and control.7.离散控制可以减少系统的复杂性和成本。
Discrete control can reduce the complexity and cost of the system.8.连续控制可以实现对系统的更精细的调节和控制。
发动机全功能数字电子控制器
(3) 减轻驾驶员的负担; 减轻驾驶员的负担; (4) 提高可靠性。 提高可靠性。 ► 由于采用余度技术、故障诊断、恢复功能,而且减少 由于采用余度技术、故障诊断、恢复功能, 了超温、超转、过应力等情况, 了超温、超转、过应力等情况,使发动机的可靠性提 高。 (5) 降低成本。 降低成本。 ► 由于包括自测试、诊断、记忆等功能,可实施计算机 由于包括自测试、诊断、记忆等功能, 辅助故障诊断,给维护带来方便。 辅助故障诊断,给维护带来方便。加上更换控制装置 不需要调整运转,使发动机维修成本降低。 不需要调整运转,使发动机维修成本降低。 (6) 易于实现发动机状态监控,易于实现与飞机控制的 易于实现发动机状态监控, 一体化。 一体化。
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先进控 制概念
稳定性 寻优控制 (SSC)
发动机 智能控制 (IEC)
性能 寻优控制 (PSC)
主动失速 /喘振控制 喘振控制 (ASC)
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SSC利用控制算法减小对部件稳定性裕度的要求。 SSC利用控制算法减小对部件稳定性裕度的要求。这 利用控制算法减小对部件稳定性裕度的要求 种方法将稳定性检查加入到发动机控制逻辑中去, 种方法将稳定性检查加入到发动机控制逻辑中去,实 时地计算非稳定性影响, 时地计算非稳定性影响,对风扇和压气机稳定性进行 在线评估,允许控制系统将喘振裕度减至最小, 在线评估,允许控制系统将喘振裕度减至最小,从而 提高发动机性能。 提高发动机性能。 IEC采用的基本方法是进行涡轮发动机的模型仿真, IEC采用的基本方法是进行涡轮发动机的模型仿真, 采用的基本方法是进行涡轮发动机的模型仿真 即将所建立的发动机模型加到推进系统的控制中去, 即将所建立的发动机模型加到推进系统的控制中去, 直接控制推力和发动机限制参数。 直接控制推力和发动机限制参数。
CrustCrawler DYNAMIXEL MX-64R 智能机械臂电机说明书
DYNAMIXEL MX-64R**Cautions- MX-64R supports RS-485 communication.- Recommended voltage of MX-64 is different with that of former RX-64.(Operating Voltage : 10~14.8V (Recommended Voltage 12V)** DESCRIPTION•DYNAMIXEL is a robot exclusive smart actuator with fully integrated DC Motor + Reduction Gearhead + Controller + Driver + Network in one DC servo module.•MX series is a new concept of DYNAMIXEL with advanced functions like precise control, PID control, 360 degree of position control and high speed communication.** CHARACTERISTIC•Advanced durability, degree of precision, and wider control zone were achieved thanks to newly applied CONTACTLESS ABSOLUTE ENCODER•360¡Æ POSITION CONTROL without dead zone•4,096 PRECISE RESOLUTION by 0.088¡Æ•SPEED CONTROL at ENDLESS TURN MODE•Reliability and accuracy were advanced in the position control through PID CONTROL•High baud rate up to 3Mbps•RS-485 LEVEL COMMUNICATION•Torque control via current sensing**The assembly structure of the MX-64 and RX-64 are the same but there some modifications to the case.**** INCLUDESDescription Qty DYNAMIXEL MX-64R 1 HORN HN05-N101 (MX Exclusive) 1 WASHER Thrust Washer 1CABLE 4P Cable 200mm 1Wrench Bolt M2.5*4 16pcs BOLT/NUTWrench Bolt M3*8 1pcsNut M2.5 18pcs** H/W SPECSProduct Name MX-64RWeight 126gDimension 40.2mm x 61.1mm x 41mmGear Ratio 200 : 1Operation Voltage (V) 10 12 14.8 Stall Torque (N.m) 5.567.3 Stall Current (A) 3.9 4.1 5.2 No Load Speed (RPM)586378 Motor Maxon MotorMinimum ControlAngleAbout 0.088¡Æ x 4,096Operating Range Actuator Mode : 360¡Æ Wheel Mode : Endless turnOperating Voltage10~14.8V (Recommended voltage : 12V) Operating Temperature -5¡ÆC ~ 80¡ÆCCommand Signal Digital PacketProtocol Half duplex Asynchronous Serial Communication (8bit,1stop, No Parity)Link (physical)RS-485 Multi Drop Bus (daisy chain type connector)ID 254 ID (0~253) Baud Rate8000bps ~ 3MbpsFeedback Functions Position, Temperature, Load, Input Voltage, Current, etc.Material Case : Engineering Plastic Gear : Full MetalPosition Sensor Contactless absolute encoderDefault ID #1 – 57600bps** After purchase, please change ID and baud rate according to your use.** COMPATIBLE PRODUCTS- Controller : CM-2+, CM-700- Interface(I/F) : USB2Dynamixel (RS-485)- NOTICE : Not compatible with the RX-64 horn. (HN05-N101 Set / T101 Set)** CONTROLLING ENVIRONMENT- Software for Dynamixel control : ROBOPLUS - Download- C/C++, C#, Labview, MATLAB, Visual Basic et. : Library – Download**Click here to download 2D and 3D drawings**Click here to go to e-Manual.。
Digital Control of Dynamic Systems 动态系统的数字控制实验答案
experiment 1:The heat exchanger has the approximate transfer functiondesign a PID controller so that the closed-loop system has a rise time tr<15sec and overshoot MP<10% to a step input command. Use MATLAB and plot its step response, then change PID’s three parameters (K,TI and Td) from large to small respectively and plot the responses, summarize their control effect. 1. 由)1)(110)(14(4)(+++=s s s s G(1) 设sT s s ks T s s k s T s T s T T k s T s T k s D I I I I I D D I 11440)110)(14(1)11()(22++=++=++=++=由上式可得 14=I T1440=D T 又由222221414144)()(1)()()(nn s s k s s ks G s D s G s D s H ωξωω++=++=∙+∙=得12=ξω1442k =ω加之%10<p M则54.0)1(6.0=->P M ξ158.1t nr <=ω则12.0t 8.1rn =>ω 故取8.0=n ω那么又由此可得625.0=ξ 24.2=k综上所求得连续PID 控制器)14401411(24.2)(s s s D ++=(连续PID 控制器)或者ss s s D 14)110)(14(24.2)(++=)1)(110)(14(4)(+++=s s s s G(2)根据G(s)和求出的D(s)用Matlab 编程如下:np=4;dp=conv(conv([4,1],[10,1]),[1,1]); ant=tf(np,dp);nc=conv([4,1],[10*2.24,1*2.24]); dc=[14,0];lead1=tf(nc,dc); sysol=lead1*ant;gcg=feedback(sysol,1); step(gcg)运行的结果如下:实验2等效法设计数字控制器已知对象传递函数 )16(1)(+=s s s G及以下指标:1、阶跃响应的超调低于10%;2、调节时间小于10秒;3、对于斜率为0.01rad/sec 的速度输入跟踪误差小于0.01rad ; 为系统设计一连续的控制器,之后分别取采样周期为上升时间的六分之一和十分之一对该控制器进行离散等效,用编程方式编写相应的仿真程序,获得仿真结果,对结果进行分析。
现代控制系统第十二版英文版课程设计
Modern Control Systems 12th Edition Course Design (EnglishVersion)IntroductionThe objective of this course design is to provide an overview of modern control systems using the Modern Control Systems 12th Edition textbook. Modern control systems are an integral part of any engineering discipline and this course ms to provide students with an in-depth understanding of fundamental control concepts, techniques and technologies that can be applied to a wide range of industrial applications. The course is designed to be delivered over 5 weeks, with a total of 20 sessions spanning lectures, discussions, and hands-on tutorials. The course design follows a syllabus that covers the following topics:1.Introduction to Control Systems2.Dynamic Models of Systems3.Feedback Control Systems4.The Time-Domn Response of Dynamical Systems5.The Frequency-Domn Response of Dynamical Systems6.Stability of Linear Control Systems7.Control System Design using Root Locus8.Control System Design using Frequency ResponseAnalysis9.State Space Analysis of Control Systems10.Digital Control Systems11.Nonlinear Systems and Control12.Control Systems ApplicationsLearning ObjectivesUpon completion of this course, students will be expected to do the following:1.Gn knowledge and understanding of the basicprinciples and concepts of control systems.2.Understand the mathematical modelling of dynamicsystems and the formulation of feedback control systems.3.Analyze and interpret the time-domn response ofdynamic systems.4.Analyze and interpret the frequency-domn responseof dynamical systems.5.Understand the stability concepts of linear controlsystems and use them in control system design.e root locus for control system design.e frequency-response analysis for control systemdesign.8.Understand the state space analysis of controlsystems.9.Understand digital control systems.10.Analyze and interpret the nonlinear systemsand control.11.Apply control systems and techniques toindustrial applications.Course OverviewThe course will be delivered over five weeks comprising of 20 sessions. Each session will last approximately 2 hours and will take place twice a week. The course structure is as follows:Week 1Session 1: Introduction to Control Systems (Chapter 1) •Overview of control systems and their applications.•Open-loop and closed-loop control systems.•Feedback control systems.•Modelling of dynamic systems and transfer functions.Session 2: Dynamic Models of Systems (Chapter 2)•Mathematical models of physical systems.•Block diagram representation of systems.•Time-domn analysis of systems.•State space representation of systems.Week 2Session 3: Feedback Control Systems (Chapter 3)•Control system objectives and performance.•Laplace transforms.•Transfer functions and Block diagrams.•Error criteria and tracking systems.Session 4: The Time-Domn Response of Dynamical Systems (Chapter 4)•Time-domn specifications.•Steady-state errors and stability.•Routh-Hurwitz stability criterion.•Root locus and the design of PI and PID controllers.Week 3Session 5: The Frequency-Domn Response of DynamicalSystems (Chapter 5)•Frequency-domn analysis of linear systems.•Bode plots.•Nyquist criterion.•Stability margins.Session 6: Stability of Linear Control Systems (Chapter 6) •Stability analysis of control systems.•Root locus and the design of lead and lag compensators.•Introduction to control system design using state space methods.Week 4Session 7: Control System Design using Root Locus (Chapter 7)•Control system design using the root locus method.•Lead and lag compensation design.•Root locus design using Matlab.Session 8: Control System Design using Frequency Response Analysis (Chapter 8)•Control system design using the frequency response methods.•Bode plots and Nyquist criterion.•Design using Matlab.Week 5Session 9: State Space Analysis of Control Systems (Chapter 9)•State space analysis of control systems.•System controllability and observability.•Pole placement design.Session 10: Digital Control Systems (Chapter 10) •Sampling and Reconstruction.•The Z-Transform.•Digital Control Systems.Session 11: Nonlinear Systems and Control (Chapter 11) •Nonlinear systems and their analysis.•Phase plane analysis.•Control of nonlinear systems.Session 12: Control Systems Applications (Chapter 12) •Industrial applications of control systems.•Case studies in automotive, process control, robotics, and aerospace.ConclusionThis course design provides students with an in-depth understanding of modern control systems and theirapplications in a wide range of industrial sectors. The syllabus is designed to cover all the fundamental control concepts, techniques and technologies that would be of relevance in any engineering discipline. By mastering the concepts and techniques in the course, students should be able to design, analyze and apply control systems to real-world problems. The course design incorporates practical applications using Matlab to enable students to simulate and experiment with control systems and get hands-on experience.。
FeedbackControlofDynamicSystems课程设计
Feedback Control of Dynamic Systems课程设计一、设计目的Feedback Control of Dynamic Systems是一门重要的控制理论课程,它是将数学和工程学的知识相结合,用于解决系统控制、操纵和稳定性问题的学科。
这门课程设计旨在帮助学生对系统控制问题有更深入的理解和认识,增强学生对系统控制算法和理论的掌握,并提高学生的实际应用能力和工程实践能力。
二、教学内容和要求本课程设计内容主要包括以下几个方面:1.设计一个系统的数学模型,包括控制环节和被控环节的变量描述,分析系统的稳态性和动态性。
2.使用传统PID控制算法或者现代控制算法设计一个系统反馈控制器,达到对目标变量的控制要求,并分析控制效果和控制稳定性。
3.对系统进行建模和仿真分析,通过仿真验证控制算法的正确性和可行性,分析系统的特点与性能,优化系统的控制策略。
4.进行实验验证,包括建立实际控制系统、平台搭建、调试,并观察系统的实际控制状态和性能。
教学要求:1.完成所分配的仿真和实验任务,并准备演示和展示。
2.操作准确、流程清晰、结果准确。
3.能够独立思考、理论分析、判断和解决问题,具备较好的创新能力。
三、设计流程本课程设计采用假设情景模型来设计,针对一个具体领域的技术问题,让学生从建模、控制算法、仿真分析和实验验证等几个方面,全面掌握反馈控制理论和方法在实际工程中的应用。
例如,假设情景模型是对一个物理传感器输出信号进行处理和控制,设计流程包括以下几个步骤:步骤一:建立传感器控制系统的数学模型1.确定控制参数:包括被控变量和控制变量的选择,传感器控制的目的和要求。
2.系统建模:建立传感器控制系统的数学模型,包括动态特性的描述、控制策略和算法的选型。
步骤二:系统控制器的设计1.控制器设计:根据传感器控制系统的数学模型,设计一个传统PID算法或现代控制算法控制器。
2.参数调整:执行参数调整策略,对控制器参数进行优化和集成,使控制系统能够快速响应和保持稳定的状态。
自适应控制(研究生经典教材)
自适应控制Adaptive control1.关于控制2.关于自适应控制3.模型参考自适应控制4.自校正控制5.自适应替代方案6.预测控制参考文献主要章节内容说明:第一部分:第一章自适应律的设计§1.参数最优化方法§2.基于Lyapunov稳定性理论的方法§3.超稳定性理论在自适应控制中的应用第二章误差模型§1.Narendra误差模型§2.增广矩阵§3.线性误差模型第三章MRAC的设计和实现第四章小结第二部分:第一章模型辨识及控制器设计§1.系统模型:CARMA模型§2.参数估计:LS法§3.控制器的设计方法:利用传递函数模型§4.自校正第二章最小方差自校正控制§1.最小方差自校正调节器§2.广义最小方差自校正控制第三章极点配置自校正控制§1.间接自校正§2.直接自校正1.About control engineering education1)control curriculum basic concept(1)dynamic system●The processes and plants that are controlled have responses that evolvein time with memory of past responses●The most common mathematical tool used to describe dynamic system isthe ordinary differential equation (ODE).●First approximate the equation as linear and time-invariant. Thenextensions can be made from this foundation that are nonlinear 、time-varying、sampled-data、distributed parameter and so on.●Method of building model (or equation )a)Idea of writing equations of motion based on the physics andchemistry of the situation.b)That of system identification based on experimental data.●Part of understanding the dynamical system requires understanding theperformance limitations and expectation of the system.2.stabilityWith stability, the system can at least be used●Classical control design method, are based on a stability test.Root locus 根轨迹Bode‟s frequency response 波特图Nyquist stability criterion 奈奎斯特判据●Optimal control, especially linear-quadratic Gaussian (LQG) control (线性二次型高斯问题) was always haunted by the fact that method did notinclude a guarantee of margin of stability.The theory and techniques of robust (鲁棒)design have been developedas alternative to LQG●In the realm of nonlinear control, including adaptive control, it iscommon practice to base the design on Lyapunov function in order to beable to guarantee stability of final result.3.feedbackMany open-loop devices such as programmable logic controllers (PLC) are in use, their design and use are not part of control engineering.●The introduction of feedback brings costs as well as benefits. Among thecosts are need for both actuators and sensors, especially sensors.●Actuator defines the control authority and set the limits of speed indynamic response.●Sensor via their inevitable noise, limit the ultimate(最终) accuracy ofcontrol within these limits, feedback affords the benefit of improveddynamic response and stability margins, improved disturbancerejection(拒绝) ,and improved robustness to parameter variability.●The trade off between costs and benefits of feedback is at the center ofcontrol design.4.Dynamic compensation●In beginning there was PID compensation, today remaining a widely usedelement of control, especially in the process control.●Other compensation approaches : lead-and-log networks (超前-滞后)observer-based compensators include : pole placement, LQG designs.●Of increasing interest are designs capable of including trade-off amongstability, dynamic response and parameter robustness.Include: Q parameterization, adaptive schemes.Such as self-tuning regulators, neural-network-based-controllers.二、historical perspectives (透视)●Most of early control manifestations appear as simple on-off (bang-bang)controllers with empirical (实验;经验性的) setting much dependent uponexperience.●The following advances such as Routhis and Hurwitz stability analysis(1877).Lyapunov‟s state model and nonlinear stability criteria(判据) (1890) .Sperry‟s early work on gyroscope and autopilots (1910), and Sikorsky‟swork on ship steering (1923)Take differential equation, Heaviside operators and Laplace transform astheir tools.●电机工程(electrical engineering)The largely changed in the late 1920s and 1930s with Black‟s developmentof the feedback electronic amplifier, Bush‟s differential analyzer, Nyquist‟sstability criterion and Bode‟s frequency response methods.The electrical engineering problems faced usually had vary complex albeitmostly linear model and had arbitrary (独立的;随机的) and wide-ringingdynamics.●过程控制(process control in chemical engineering)Most of the progress controlled were complex and highly nonlinear, butusually had relatively docile (易于处理的) dynamics.One major outcome of this type of work was Ziegler-Nichols‟PIDthres-term controller. This control approach is still in use today, worldwidewith relatively minor modifications and upgrades (including sampled dataPID controllers with feed forward control, anti-integrator-windupcontrollers :抗积分饱和,and fuzzy logic implementations).●机械工程(mechanical engineering)The application of controls in mechanical engineering dealt mostly in thebeginning with mechanism controls, such as servomechanisms, governorsand robots.Some typical control application areas now include manufacturing processcontrols, vehicle dynamic and safety control, biomedical devices and geneticprocess research.Some early methodological outcomes were the olden burger-Kahenbugerdescribing function method of equivalent linearization, and minimum-time,bang-bang control.●航空工程(aeronautical engineering )The problems were generally a hybrid (混合) of well-modeled mechanicsplus marginally understood fluid dynamics. The models were often weaklynonlinear, and the dynamics were sometimes unstable.Major contributions to framework of controls as discipline were Evan‟s rootlocus (1948) and gain-scheduling.●Additional major contributions to growth of the discipline of control over thelast 30-40 years have tended to be independent of traditional disciplines.Examples include:Pontryagin‟s maximum principle (1956) 庞特里金Bellman‟s dynamic programming (1957)贝尔曼Kalman‟s optimal estimation (1960)And the recent advances in robust control.三、Abstract thoughts on curriculum●The possibilities for topic to teach are sufficiently great. If one tries topresent proofs of all theoretical results. One is in danger of giving thestudents many mathematical details with little physical intuition orappreciation for the purposes for which the system is designed.●Control is based on two distinct streams of thought. One stream is physicaland discipline-based. Because one must always be controlling some thing.The other stream is mathematics-based, because the basis concepts ofstability and feedback are fundamentally abstract concepts best expressedmathematically. This duality(两重性) has raised, over the years, regularcomplaints about the …gap‟ between theory and practice.●The control curriculum typically begins with one or two courses designed topresent an overview of control based on linear, constant, ODE models,s-plane and Nyquist‟s stability ideas, SISO feedback and PID, lead-lay andpole-placement compensation.These introductory courses can then be followed by courses in linear systemtheory, digital of control, optimal control, advanced theory of feedback, andsystem identification.四、Main control courses●Introduction to controlLumped system theoryNonlinear controlOptimal controlAdaptive controlRobot controlDigital controlModeling and simulationAdvanced theoryStochastic processesLarge scale multivariable systemManufacturing systemFuzzy logic Neural Networks外文期刊:《Automatic》IFAC 国际自动控制联合会Computer and control abstractsIEEE translations on Automatic controlAutomation●Specialized \ experimental courses✓Intelligent controlApplication of Artificial IntelligenceSimulation and optimization of lager scale systems robust control ✓System identification✓Microcomputer-based control systemDiscrete-event systemsParallel and Distributed computationNumerical optimization methodsNumerical system theory●Top key works from 1963-1995 in IIACAdaptive control 305Optimal control 277Identification 255Parameter estimation 244Stability 217Linear system 184Non-linear systems 168Robust control 158Discrete-time systems 143Multivariable systems 140Robustness 140Multivariable systems control systems 110Optimization 110Computer control 104Large-scale systems 103Kalman filter 102Modeling 107为什么自适应 《Astrom 》chapter 1✓ 反馈可以消除扰动。
自动化控制工程外文翻译外文文献英文文献
Team-Centered Perspective for Adaptive Automation DesignLawrence J.PrinzelLangley Research Center, Hampton, VirginiaAbstractAutomation represents a very active area of human factors research. Thejournal, Human Factors, published a special issue on automation in 1985.Since then, hundreds of scientific studies have been published examiningthe nature of automation and its interaction with human performance.However, despite a dramatic increase in research investigating humanfactors issues in aviation automation, there remain areas that need furtherexploration. This NASA Technical Memorandum describes a new area ofIt discussesautomation design and research, called “adaptive automation.” the concepts and outlines the human factors issues associated with the newmethod of adaptive function allocation. The primary focus is onhuman-centered design, and specifically on ensuring that adaptiveautomation is from a team-centered perspective. The document showsthat adaptive automation has many human factors issues common totraditional automation design. Much like the introduction of other new technologies and paradigm shifts, adaptive automation presents an opportunity to remediate current problems but poses new ones forhuman-automation interaction in aerospace operations. The review here isintended to communicate the philosophical perspective and direction ofadaptive automation research conducted under the Aerospace OperationsSystems (AOS), Physiological and Psychological Stressors and Factors (PPSF)project.Key words:Adaptive Automation; Human-Centered Design; Automation;Human FactorsIntroduction"During the 1970s and early 1980s...the concept of automating as much as possible was considered appropriate. The expected benefit was a reduction inpilot workload and increased safety...Although many of these benefits have beenrealized, serious questions have arisen and incidents/accidents that have occurredwhich question the underlying assumptions that a maximum availableautomation is ALWAYS appropriate or that we understand how to designautomated systems so that they are fully compatible with the capabilities andlimitations of the humans in the system."---- ATA, 1989The Air Transport Association of America (ATA) Flight Systems Integration Committee(1989) made the above statement in response to the proliferation of automation in aviation. They noted that technology improvements, such as the ground proximity warning system, have had dramatic benefits; others, such as the electronic library system, offer marginal benefits at best. Such observations have led many in the human factors community, most notably Charles Billings (1991; 1997) of NASA, to assert that automation should be approached from a "human-centered design" perspective.The period from 1970 to the present was marked by an increase in the use of electronic display units (EDUs); a period that Billings (1997) calls "information" and “management automation." The increased use of altitude, heading, power, and navigation displays; alerting and warning systems, such as the traffic alert and collision avoidance system (TCAS) and ground proximity warning system (GPWS; E-GPWS; TAWS); flight management systems (FMS) and flight guidance (e.g., autopilots; autothrottles) have "been accompanied by certain costs, including an increased cognitive burden on pilots, new information requirements that have required additional training, and more complex, tightly coupled, less observable systems" (Billings, 1997). As a result, human factors research in aviation has focused on the effects of information and management automation. The issues of interest include over-reliance on automation, "clumsy" automation (e.g., Wiener, 1989), digital versus analog control, skill degradation, crew coordination, and data overload (e.g., Billings, 1997). Furthermore, research has also been directed toward situational awareness (mode & state awareness; Endsley, 1994; Woods & Sarter, 1991) associated with complexity, coupling, autonomy, and inadequate feedback. Finally, human factors research has introduced new automation concepts that will need to be integrated into the existing suite of aviationautomation.Clearly, the human factors issues of automation have significant implications for safetyin aviation. However, what exactly do we mean by automation? The way we choose to define automation has considerable meaning for how we see the human role in modern aerospace s ystems. The next section considers the concept of automation, followed by an examination of human factors issues of human-automation interaction in aviation. Next, a potential remedy to the problems raised is described, called adaptive automation. Finally, the human-centered design philosophy is discussed and proposals are made for how the philosophy can be applied to this advanced form of automation. The perspective is considered in terms of the Physiological /Psychological Stressors & Factors project and directions for research on adaptive automation.Automation in Modern AviationDefinition.Automation refers to "...systems or methods in which many of the processes of production are automatically performed or controlled by autonomous machines or electronic devices" (Parsons, 1985). Automation is a tool, or resource, that the human operator can use to perform some task that would be difficult or impossible without machine aiding (Billings, 1997). Therefore, automation can be thought of as a process of substituting the activity of some device or machine for some human activity; or it can be thought of as a state of technological development (Parsons, 1985). However, some people (e.g., Woods, 1996) have questioned whether automation should be viewed as a substitution of one agent for another (see "apparent simplicity, real complexity" below). Nevertheless, the presence of automation has pervaded almost every aspect of modern lives. From the wheel to the modern jet aircraft, humans have sought to improve the quality of life. We have built machines and systems that not only make work easier, more efficient, and safe, but also give us more leisure time. The advent of automation has further enabled us to achieve this end. With automation, machines can now perform many of the activities that we once had to do. Our automobile transmission will shift gears for us. Our airplanes will fly themselves for us. All we have to dois turn the machine on and off. It has even been suggested that one day there may not be aaccidents resulting from need for us to do even that. However, the increase in “cognitive” faulty human-automation interaction have led many in the human factors community to conclude that such a statement may be premature.Automation Accidents. A number of aviation accidents and incidents have been directly attributed to automation. Examples of such in aviation mishaps include (from Billings, 1997):DC-10 landing in control wheel steering A330 accident at ToulouseB-747 upset over Pacific DC-10 overrun at JFK, New YorkB-747 uncommandedroll,Nakina,Ont. A320 accident at Mulhouse-HabsheimA320 accident at Strasbourg A300 accident at NagoyaB-757 accident at Cali, Columbia A320 accident at BangaloreA320 landing at Hong Kong B-737 wet runway overrunsA320 overrun at Warsaw B-757 climbout at ManchesterA310 approach at Orly DC-9 wind shear at CharlotteBillings (1997) notes that each of these accidents has a different etiology, and that human factors investigation of causes show the matter to be complex. However, what is clear is that the percentage of accident causes has fundamentally shifted from machine-caused to human-caused (estimations of 60-80% due to human error) etiologies, and the shift is attributable to the change in types of automation that have evolved in aviation.Types of AutomationThere are a number of different types of automation and the descriptions of them vary considerably. Billings (1997) offers the following types of automation:?Open-Loop Mechanical or Electronic Control.Automation is controlled by gravity or spring motors driving gears and cams that allow continous and repetitive motion. Positioning, forcing, and timing were dictated by the mechanism and environmental factors (e.g., wind). The automation of factories during the Industrial Revolution would represent this type of automation.?Classic Linear Feedback Control.Automation is controlled as a function of differences between a reference setting of desired output and the actual output. Changes a re made to system parameters to re-set the automation to conformance. An example of this type of automation would be flyball governor on the steam engine. What engineers call conventional proportional-integral-derivative (PID) control would also fit in this category of automation.?Optimal Control. A computer-based model of controlled processes i s driven by the same control inputs as that used to control the automated process. T he model output is used to project future states and is thus used to determine the next control input. A "Kalman filtering" approach is used to estimate the system state to determine what the best control input should be.?Adaptive Control. This type of automation actually represents a number of approaches to controlling automation, but usually stands for automation that changes dynamically in response to a change in state. Examples include the use of "crisp" and "fuzzy" controllers, neural networks, dynamic control, and many other nonlinear methods.Levels of AutomationIn addition to “types ” of automation, we can also conceptualize different “levels ” of automation control that the operator can have. A number of taxonomies have been put forth, but perhaps the best known is the one proposed by Tom Sheridan of Massachusetts Institute of Technology (MIT). Sheridan (1987) listed 10 levels of automation control:1. The computer offers no assistance, the human must do it all2. The computer offers a complete set of action alternatives3. The computer narrows the selection down to a few4. The computer suggests a selection, and5. Executes that suggestion if the human approves, or6. Allows the human a restricted time to veto before automatic execution, or7. Executes automatically, then necessarily informs the human, or8. Informs the human after execution only if he asks, or9. Informs the human after execution if it, the computer, decides to10. The computer decides everything and acts autonomously, ignoring the humanThe list covers the automation gamut from fully manual to fully automatic. Although different researchers define adaptive automation differently across these levels, the consensus is that adaptive automation can represent anything from Level 3 to Level 9. However, what makes adaptive automation different is the philosophy of the approach taken to initiate adaptive function allocation and how such an approach may address t he impact of current automation technology.Impact of Automation TechnologyAdvantages of Automation . Wiener (1980; 1989) noted a number of advantages to automating human-machine systems. These include increased capacity and productivity, reduction of small errors, reduction of manual workload and mental fatigue, relief from routine operations, more precise handling of routine operations, economical use of machines, and decrease of performance variation due to individual differences. Wiener and Curry (1980) listed eight reasons for the increase in flight-deck automation: (a) Increase in available technology, such as FMS, Ground Proximity Warning System (GPWS), Traffic Alert andCollision Avoidance System (TCAS), etc.; (b) concern for safety; (c) economy, maintenance, and reliability; (d) workload reduction and two-pilot transport aircraft certification; (e) flight maneuvers and navigation precision; (f) display flexibility; (g) economy of cockpit space; and (h) special requirements for military missions.Disadvantages o f Automation. Automation also has a number of disadvantages that have been noted. Automation increases the burdens and complexities for those responsible for operating, troubleshooting, and managing systems. Woods (1996) stated that automation is "...a wrapped package -- a package that consists of many different dimensions bundled together as a hardware/software system. When new automated systems are introduced into a field of practice, change is precipitated along multiple dimensions." As Woods (1996) noted, some of these changes include: ( a) adds to or changes the task, such as device setup and initialization, configuration control, and operating sequences; (b) changes cognitive demands, such as requirements for increased situational awareness; (c) changes the roles of people in the system, often relegating people to supervisory controllers; (d) automation increases coupling and integration among parts of a system often resulting in data overload and "transparency"; and (e) the adverse impacts of automation is often not appreciated by those who advocate the technology. These changes can result in lower job satisfaction (automation seen as dehumanizing human roles), lowered vigilance, fault-intolerant systems, silent failures, an increase in cognitive workload, automation-induced failures, over-reliance, complacency, decreased trust, manual skill erosion, false alarms, and a decrease in mode awareness (Wiener, 1989).Adaptive AutomationDisadvantages of automation have resulted in increased interest in advanced automation concepts. One of these concepts is automation that is dynamic or adaptive in nature (Hancock & Chignell, 1987; Morrison, Gluckman, & Deaton, 1991; Rouse, 1977; 1988). In an aviation context, adaptive automation control of tasks can be passed back and forth between the pilot and automated systems in response to the changing task demands of modern aircraft. Consequently, this allows for the restructuring of the task environment based upon (a) what is automated, (b) when it should be automated, and (c) how it is automated (Rouse, 1988; Scerbo, 1996). Rouse(1988) described criteria for adaptive aiding systems:The level of aiding, as well as the ways in which human and aidinteract, should change as task demands vary. More specifically,the level of aiding should increase as task demands become suchthat human performance will unacceptably degrade withoutaiding. Further, the ways in which human and aid interact shouldbecome increasingly streamlined as task demands increase.Finally, it is quite likely that variations in level of aiding andmodes of interaction will have to be initiated by the aid rather thanby the human whose excess task demands have created a situationrequiring aiding. The term adaptive aiding is used to denote aidingconcepts that meet [these] requirements.Adaptive aiding attempts to optimize the allocation of tasks by creating a mechanism for determining when tasks need to be automated (Morrison, Cohen, & Gluckman, 1993). In adaptive automation, the level or mode of automation can be modified in real time. Further, unlike traditional forms of automation, both the system and the pilot share control over changes in the state of automation (Scerbo, 1994; 1996). Parasuraman, Bahri, Deaton, Morrison, and Barnes (1992) have argued that adaptive automation represents the optimal coupling of the level of pilot workload to the level of automation in the tasks. Thus, adaptive automation invokes automation only when task demands exceed the pilot's capabilities. Otherwise, the pilot retains manual control of the system functions. Although concerns have been raised about the dangers of adaptive automation (Billings & Woods, 1994; Wiener, 1989), it promises to regulate workload, bolster situational awareness, enhance vigilance, maintain manual skill levels, increase task involvement, and generally improve pilot performance.Strategies for Invoking AutomationPerhaps the most critical challenge facing system designers seeking to implement automation concerns how changes among modes or levels of automation will be accomplished (Parasuraman e t al., 1992; Scerbo, 1996). Traditional forms of automation usually start with some task or functional analysis and attempt to fit the operational tasks necessary to the abilities of the human or the system. The approach often takes the form of a functional allocation analysis (e.g., Fitt's List) in which an attempt is made to determine whether the human or the system is better suited to do each task. However, many in the field have pointed out the problem with trying to equate the two in automated systems, as each have special characteristics that impede simple classification taxonomies. Such ideas as these have led some to suggest other ways of determining human-automation mixes. Although certainly not exhaustive, some of these ideas are presented below.Dynamic Workload Assessment.One approach involves the dynamic assessment o fmeasures t hat index the operators' state of mental engagement. (Parasuraman e t al., 1992; Rouse,1988). The question, however, is what the "trigger" should be for the allocation of functions between the pilot and the automation system. Numerous researchers have suggested that adaptive systems respond to variations in operator workload (Hancock & Chignell, 1987; 1988; Hancock, Chignell & Lowenthal, 1985; Humphrey & Kramer, 1994; Reising, 1985; Riley, 1985; Rouse, 1977), and that measures o f workload be used to initiate changes in automation modes. Such measures include primary and secondary-task measures, subjective workload measures, a nd physiological measures. T he question, however, is what adaptive mechanism should be used to determine operator mental workload (Scerbo, 1996).Performance Measures. One criterion would be to monitor the performance of the operator (Hancock & Chignel, 1987). Some criteria for performance would be specified in the system parameters, and the degree to which the operator deviates from the criteria (i.e., errors), the system would invoke levels of adaptive automation. For example, Kaber, Prinzel, Clammann, & Wright (2002) used secondary task measures to invoke adaptive automation to help with information processing of air traffic controllers. As Scerbo (1996) noted, however,"...such an approach would be of limited utility because the system would be entirely reactive."Psychophysiological M easures.Another criterion would be the cognitive and attentional state of the operator as measured by psychophysiological measures (Byrne & Parasuraman, 1996). An example of such an approach is that by Pope, Bogart, and Bartolome (1996) and Prinzel, Freeman, Scerbo, Mikulka, and Pope (2000) who used a closed-loop system to dynamically regulate the level of "engagement" that the subject had with a tracking task. The system indexes engagement on the basis of EEG brainwave patterns.Human Performance Modeling.Another approach would be to model the performance of the operator. The approach would allow the system to develop a number of standards for operator performance that are derived from models of the operator. An example is Card, Moran, and Newell (1987) discussion of a "model human processor." They discussed aspects of the human processor that could be used to model various levels of human performance. Another example is Geddes (1985) and his colleagues (Rouse, Geddes, & Curry, 1987-1988) who provided a model to invoke automation based upon system information, the environment, and expected operator behaviors (Scerbo, 1996).Mission Analysis. A final strategy would be to monitor the activities of the mission or task (Morrison & Gluckman, 1994). Although this method of adaptive automation may be themost accessible at the current state of technology, Bahri et al. (1992) stated that such monitoring systems lack sophistication and are not well integrated and coupled to monitor operator workload or performance (Scerbo, 1996). An example of a mission analysis approach to adaptive automation is Barnes and Grossman (1985) who developed a system that uses critical events to allocate among automation modes. In this system, the detection of critical events, such as emergency situations or high workload periods, invoked automation.Adaptive Automation Human Factors IssuesA number of issues, however, have been raised by the use of adaptive automation, and many of these issues are the same as those raised almost 20 years ago by Curry and Wiener (1980). Therefore, these issues are applicable not only to advanced automation concepts, such as adaptive automation, but to traditional forms of automation already in place in complex systems (e.g., airplanes, trains, process control).Although certainly one can make the case that adaptive automation is "dressed up" automation and therefore has many of the same problems, it is also important to note that the trend towards such forms of automation does have unique issues that accompany it. As Billings & Woods (1994) stated, "[i]n high-risk, dynamic environments...technology-centered automation has tended to decrease human involvement in system tasks, and has thus impaired human situation awareness; both are unwanted consequences of today's system designs, but both are dangerous in high-risk systems. [At its present state of development,] adaptive ("self-adapting") automation represents a potentially serious threat ... to the authority that the human pilot must have to fulfill his or her responsibility for flight safety."The Need for Human Factors Research.Nevertheless, such concerns should not preclude us from researching the impact that such forms of advanced automation are sure to have on human performance. Consider Hancock’s (1996; 1997) examination of the "teleology for technology." He suggests that automation shall continue to impact our lives requiring humans to co-evolve with the technology; Hancock called this "techneology."What Peter Hancock attempts to communicate to the human factors community is that automation will continue to evolve whether or not human factors chooses to be part of it. As Wiener and Curry (1980) conclude: "The rapid pace of automation is outstripping one's ability to comprehend all the implications for crew performance. It is unrealistic to call for a halt to cockpit automation until the manifestations are completely understood. We do, however, call for those designing, analyzing, and installing automatic systems in the cockpit to do so carefully; to recognize the behavioral effects of automation; to avail themselves of present andfuture guidelines; and to be watchful for symptoms that might appear in training andoperational settings." The concerns they raised are as valid today as they were 23 years ago.However, this should not be taken to mean that we should capitulate. Instead, becauseobservation suggests that it may be impossible to fully research any new Wiener and Curry’stechnology before implementation, we need to form a taxonomy and research plan tomaximize human factors input for concurrent engineering of adaptive automation.Classification of Human Factors Issues. Kantowitz and Campbell (1996)identified some of the key human factors issues to be considered in the design of advancedautomated systems. These include allocation of function, stimulus-response compatibility, andmental models. Scerbo (1996) further suggested the need for research on teams,communication, and training and practice in adaptive automated systems design. The impactof adaptive automation systems on monitoring behavior, situational awareness, skilldegradation, and social dynamics also needs to be investigated. Generally however, Billings(1997) stated that the problems of automation share one or more of the followingcharacteristics: Brittleness, opacity, literalism, clumsiness, monitoring requirement, and dataoverload. These characteristics should inform design guidelines for the development, analysis,and implementation of adaptive automation technologies. The characteristics are defined as: ?Brittleness refers to "...an attribute of a system that works well under normal or usual conditions but that does not have desired behavior at or close to some margin of its operating envelope."?Opacity reflects the degree of understanding of how and why automation functions as it does. The term is closely associated with "mode awareness" (Sarter & Woods, 1994), "transparency"; or "virtuality" (Schneiderman, 1992).?Literalism concern the "narrow-mindedness" of the automated system; that is, theflexibility of the system to respond to novel events.?Clumsiness was coined by Wiener (1989) to refer to automation that reduced workload demands when the demands are already low (e.g., transit flight phase), but increases them when attention and resources are needed elsewhere (e.g., descent phase of flight). An example is when the co-pilot needs to re-program the FMS, to change the plane's descent path, at a time when the co-pilot should be scanning for other planes.?Monitoring requirement refers to the behavioral and cognitive costs associated withincreased "supervisory control" (Sheridan, 1987; 1991).?Data overload points to the increase in information in modern automated contexts (Billings, 1997).These characteristics of automation have relevance for defining the scope of humanfactors issues likely to plague adaptive automation design if significant attention is notdirected toward ensuring human-centered design. The human factors research communityhas noted that these characteristics can lead to human factors issues of allocation of function(i.e., when and how should functions be allocated adaptively); stimulus-response compatibility and new error modes; how adaptive automation will affect mental models,situation models, and representational models; concerns about mode unawareness and-of-the-loop” performance problem; situation awareness decay; manual skill decay and the “outclumsy automation and task/workload management; and issues related to the design of automation. This last issue points to the significant concern in the human factors communityof how to design adaptive automation so that it reflects what has been called “team-centered”;that is, successful adaptive automation will l ikely embody the concept of the “electronic team member”. However, past research (e.g., Pilots Associate Program) has shown that designing automation to reflect such a role has significantly different requirements than those arising in traditional automation design. The field is currently focused on answering the questions,does that definition translate into“what is it that defines one as a team member?” and “howUnfortunately, the literature also shows that the designing automation to reflect that role?” answer is not transparent and, therefore, adaptive automation must first tackle its own uniqueand difficult problems before it may be considered a viable prescription to currenthuman-automation interaction problems. The next section describes the concept of the electronic team member and then discusses t he literature with regard to team dynamics, coordination, communication, shared mental models, and the implications of these foradaptive automation design.Adaptive Automation as Electronic Team MemberLayton, Smith, and McCoy (1994) stated that the design of automated systems should befrom a team-centered approach; the design should allow for the coordination betweenmachine agents and human practitioners. However, many researchers have noted that automated systems tend to fail as team players (Billings, 1991; Malin & Schreckenghost,1992; Malin et al., 1991;Sarter & Woods, 1994; Scerbo, 1994; 1996; Woods, 1996). Thereason is what Woods (1996) calls “apparent simplicity, real complexity.”Apparent Simplicity, Real Complexity.Woods (1996) stated that conventional wisdomabout automation makes technology change seem simple. Automation can be seen as simply changing the human agent for a machine agent. Automation further provides for more optionsand methods, frees up operator time to do other things, provides new computer graphics and interfaces, and reduces human error. However, the reality is that technology change has often。
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experiment 1:
The heat exchanger has the approximate transfer function
design a PID controller so that the closed-loop system has a rise time tr<15sec and overshoot MP<10% to a step input e MATLAB and plot its step response,then change PID’s t hree parameters(K,TI and Td) from large to small respectively and plot the responses, summarize their control effect. 1. 由)
1)(110)(14(4
)(+++=
s s s s G
(1) 设
s
T s s k
s T s s k s T s T s T T k s T s T k s D I I I I I D D I 1
1440)110)(14(1)11()(22++=++=++=++=由上式可得 14=I T 14
40
=
D T 又由2
2
2221414144)()(1)()()(n
n s s k s s k
s G s D s G s D s H ωξωω++=++=•+•=
得12=ξω14
42
k =ω
加之%10<p M
则54.0)1(6.0=->P M ξ
158
.1t n
r <=
ω则12.0t 8
.1r
n =>
ω 故取
8.0=n ω
那么又由此可得625.0=ξ24.2=k
综上所求得连续PID 控制器)14
401411(24.2)(s s s D ++=(连续PID 控制器)
或者s
s s s D 14)
110)(14(24.2)(++=
)
1)(110)(14(4
)(+++=
s s s s G
(2)根据G(s)和求出的D(s)用Matlab 编程如下:
np=4;
dp=conv(conv([4,1],[10,1]),[1,1]); ant=tf(np,dp);
nc=conv([4,1],[10*2.24,1*2.24]); dc=[14,0];
lead1=tf(nc,dc); sysol=lead1*ant;
gcg=feedback(sysol,1); step(gcg)
运行的结果如下:
实验2 等效法设计数字控制器
已知对象传递函数)
16(1
)(+=
s s s G
及以下指标:
1、阶跃响应的超调低于10%;
2、调节时间小于10秒;
3、对于斜率为0.01rad/sec 的速度输入跟踪误差小于0.01rad ;
为系统设计一连续的控制器,之后分别取采样周期为上升时间的六分之一和十分之一对该控制器进行离散等效,用编程方式编写相应的仿真程序,获得仿真结果,对结果进行分析。
1. 求解D(s)
由)
16(1
)(+=
s s s G
设a
s s k s D ++=)
16()( 于是H(s)=
k
as s k
s G s D s G s D ++=•+•2
)()(1)()((1)
又2
22
2)(ωξωω++=s s s H
(2)
通过(1)、(2)式可推得 2
ω=k ξω2=a 其中满足
(1)%10<p M
则54.0)1(6.0=->p M ξ
(2)s t s 10<
则s
n t 6
.4>
ξω=0.46
(3)1a k
a
s k D G s lim k 1k ,101
.001.0e r k im 0s v v ss 0v >=+=
••=>=>=
→l 即 2ω=k ξω
2=a 12>ξ
ω
综合取
6.0=ξ
2.1n =ω
44.1k 2n ==ω
44.12a ==ξω
所以
44
.1s 1
s 644
.1)s (D ++=
2. 编程求step 响应
程序exp21c.m np=1;
dp=[6 1 0];
ant=tf(np,dp);
nc=[6*1.44 1*1.44];
dc=[1 1.44];
lead1=tf(nc,dc);
sysol=lead1*ant;
syscl=feedback(sysol,1);
step(syscl,'r')
t 1.55(s)
其中
r
e是否满足要求3.编程求ramp响应,并观察
ss
程序exp22c.m
np=1;
dp=[6 1 0];
ant=tf(np,dp);
nc=[6*1.44 1*1.44];
dc=[1 1.44];
lead1=tf(nc,dc);
sysol=lead1*ant;
syscl=feedback(sysol,1);
t=[1:0.1:20];
u=0.01*t; lsim(syscl,u,t)
4. 用mat 中的函数实现数字控制系统,10
r
t T
=0.155(s ) (1) 程序exp22dmatch.m
%T=0.155,matched np=1;
dp=[6 1 0]; ant=tf(np,dp);
nc=[6*1.44 1*1.44]; dc=[1 1.44]; lead1=tf(nc,dc); sysol=lead1*ant;
antd=c2d(ant,0.155,'zoh');
lead1d=c2d(lead1,0.155,'matched'); sysold=lead1d*antd; syscl=feedback(sysol,1); syscld=feedback(sysold,1);
ud=0.1*feedback(lead1d,antd,-1); step(syscl,'r',syscld,'k',ud,'b');
(2) 运行程序2得step 图
(3) 用零极点匹配(Z-O-M)的方法求D(z)
因为 44
.1s 1
s 644.1)s (D ++=
零点:6
11-
=z
极点:44.1p -=
映射后:T 6
11e z ⋅-==0.9745 T
44.1e
p -==0.8
设)
8.0z ()
9745.0z (K )z (D p
--=
又由144
.144
.1)s (D )8.0z ()9745.0z (K )z (D 0s p
1z ===--===
由等式两边得=P K 7.8431 其中算得=)(z D 0.8
-z 7.6431
z 8431.78.0z 9745.0z 7.8431
-=
-- 1
--1
0.8z -17.6431z
8431.7)z (E )z (U -=
由MA TLAB 求得 7.844 z - 7.644
lead1d =-------------------
z - 0.8
用零阶保持(Z-O-H)的方法求G(z)
因为)
16(1
)(+=
s s s G
则)6
1(6
1)16(1)(22
+=+=⎭⎬⎫⎩⎨
⎧s s s s s s G 根据附录表B.2得
=
})
({
s
s G Z )9745.0z ()1z (6
1)00032.0z 00033.0(z 2--+ =)(z G )
z (U )z (Y 9745.0z 9745.1z 0019
.0z 002.0}s )s (G {
Z )z 1(21=+-+=-- 2
12
1z
9745.0z 9745.11z 0019.0z 002.0)z (U )z (Y ----+-+= 由MA TLAB 求得 0.001985 z + 0.001968
antd =--------------------------------
z^2 - 1.974 z + 0.9745
5. 自编程实现数字控制系统
由D(z)得差分方程 由G(z)得差分方程 程序如下exp2d.m 得
%T=0.155,matched, kk=60;
y=zeros(1,kk); e=zeros(1,kk); u=zeros(1,kk); y(2)=0; y(1)=0; e(2)=0; e(1)=0; u(2)=0; u(1)=0; for k=3:kk
y(k)=1.9745*y(k-1)-0.9745*y(k-2)+0.002*u(k-1)+0.0019*u(k-2); e(k)=1-y(k);
u(k)=0.8*u(k-1)+7.8431*e(k)-7.6431*e(k-1);
1
--1
0.8z -17.6431z
8431.7)z (E )z (U -=
end
y=y'; hold on; figure(1);
k=1:kk; stairs(k,y); figure(2) k=1:kk; plot(k,y);。