work based Learning Control
教育的便利英语作文
教育的便利英语作文下载温馨提示:该文档是我店铺精心编制而成，希望大家下载以后，能够帮助大家解决实际的问题。
文档下载后可定制随意修改，请根据实际需要进行相应的调整和使用，谢谢!并且，本店铺为大家提供各种各样类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，如想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by theeditor. I hope that after you download them,they can help yousolve practical problems. The document can be customized andmodified after downloading,please adjust and use it according toactual needs, thank you!In addition, our shop provides you with various types ofpractical materials,such as educational essays, diaryappreciation,sentence excerpts,ancient poems,classic articles,topic composition,work summary,word parsing,copyexcerpts,other materials and so on,want to know different data formats andwriting methods,please pay attention!Education is now more convenient than ever before. With the advancement of technology, learning has become more accessible and flexible. Students no longer have to rely solely on traditional classroom settings to gain knowledge. They can now access educational materials and resources anytime, anywhere, through various digital platforms.One of the conveniences of education is online learning. With just a few clicks, students can enroll in online courses and learn at their own pace. They can watch video lectures, participate in online discussions, and complete assignments all from the comfort of their own homes. This flexibility allows students to balance their studies with other commitments, such as work or family responsibilities.Another convenience is the availability of educational apps and software. These tools provide interactive and engaging learning experiences. Students can use language learning apps to practice their English skills, math appsto solve problems, and science apps to conduct virtual experiments. These apps not only make learning more enjoyable but also provide instant feedback, allowing students to track their progress and identify areas for improvement.Furthermore, the internet has made research and information gathering easier than ever. Students no longer have to spend hours in the library searching for books and journals. They can simply type a few keywords into a search engine and access a wealth of information within seconds. This convenience not only saves time but also allows students to explore different perspectives and access up-to-date information.In addition, online collaboration tools have revolutionized group work and project-based learning. Students can now collaborate with their peers fromdifferent parts of the world through video conferencing and file-sharing platforms. This not only enhances their communication and teamwork skills but also exposes them to diverse perspectives and cultures.Moreover, education has become more convenient for individuals with special needs. Assistive technologies such as screen readers, speech recognition software, and adaptive devices have made it possible for students with disabilities to access educational materials andparticipate in classroom activities. These technologies have leveled the playing field and provided equal opportunities for all learners.In conclusion, education has become more convenient due to technological advancements. Online learning, educational apps, easy access to information, online collaboration tools, and assistive technologies have all contributed to making education more accessible and flexible. These conveniences have empowered students to take control of their own learning and pursue their educational goals in a way that suits their individual needs and preferences.。
Pearson Onscreen Platform (POP) 用户指南说明书
Why are you moving these tests to the Pearson Onscreen Platform (POP)?Starting from Autumn 2017 we will be transferring all our onscreen assessments from Promissor to the Pearson Onscreen Platform (POP), as the version of Promissor which we have been using for most of our onscreen assessments will be decommissioned and will no longer be supported.POP will allow us to deliver new functions and testing improvements to centres more effectively, and to provide them with access to a wider range of delivery models via a more flexible and stable testing platform.I have previously only used Promissor before, should I be using POP LAN or POP Offline?For centres who run Promissor tests offsite on laptops we would recommend using the POP Offline test player. You download packages from the POP Assessor Dashboard and run the test offline before uploading. This is generally more rele-vant to work based learning training providers.If you generally run all of your tests on site within your centre on a network, we would recommend Pearson Onscreen Platform LAN.I already use POP to deliver some of my onscreen tests, do I need to do anything?Customers who already use POP either LAN or Offline there is no further action you need to take other than to book your tests to POP. Preparing and delivering your tests on POP will work in exactly the same way for those tests you delivered on Promissor previously. If you do already have the software installed it is worth checking that you are on the most up to date version.I already use POP, how to do I check that I am on the most recent version of the software?To check the version that you have installed follow these instructions - Open Con-trol Panel -> Programs and Features. Find the software in the list and check the version number in the last column. You can then view what the latest version is by going to the installation guide on our website.Does the process of booking tests change on the new system?You will be able book onscreen tests on Edexcel Online in the usual way. For a lim-ited time during the transition of all tests from Promissor to POP you will have the option to book your tests on either platform.This document gives you a quick guide to what Edexcel Online will look like during this time and help you confidently book your learners’ tests.How can I book a demo test?Your Account Specialist will be able to arrange a demo test for you, you can contact them by email or phone to let them know when is the most convenient time for your demo test to be available to you.I already use POP LAN for onscreen tests, do I have to download anything else?You can run all of your onscreen tests on POP LAN. You will need to ensure you have the most up to date version - information on this can be found in section 6 of the POP LAN In Depth Guide.How do I create new users?POP LAN - If this is your first time using POP (all versions) the first step to take is to complete the declaration form and return it to your account specialist. You will find the contact information for your specialist at the end of the form.For existing users, the administrator for the Administrator Dashboard software can create these.POP Offline - If this is your first time using POP (all versions) the first step to take is to complete the declaration form and return it to your account specialist. You will find the contact information for your specialist at the end of the form.If you are already using the offline version of POP, but require additional accounts for your assessors to be able to access their onscreen tests on POP Assessor Dashboard, please complete the additional user form.If you are a new POP user, but also have a large number of assessors who require an account, please complete both the declaration form and the additional user form.How do I install the Pearson Onscreen Platform?POP LAN - Once user accounts are set up (see above) you will need to run through the stages outlined in section 2 of the POP LAN in depth guidePOP Offline - If you are a WBL centre or FE college with assessors who use laptops to run one test at a time, and go out to your learners workplace, your centre should install the POP Offline test player onto assessors’ laptops (see link for details). This will allow them to manage the tests that they have booked using the POP Assessor Dashboard and download tests ahead of time.How can I apply for Access Arrangements for onscreen tests?You can apply up to 25% additional time at the point of booking your learners test on Edexcel Online. This can be done by ticking the box to add 25% additional time. If your learner requires more than 25% additional time or any other access arrangements you can send your request via the Access Arrangements Online Tool.What support or training material is available?Our support and training materials can be found on our website. There are guided video tutorials and step by step guides available to download. In addition we will also be running online training sessions on the new POP Assessor Dashboard for running offline tests - a variety of dates are available for you to book on our web-site.Can tests booked on Promissor be taken on POP?Test bookings are not transferable between the different platforms.What can I do if I have booked my tests on the wrong platform?If your test is scheduled in the future you can cancel the booking on Edexcel Online, this will then allow you to rebook the test for your learner on the platform that you are wanting to use.If the test time has passed you can contact your account specialist who will be able to remove the booking for you which will then allow you to rebook your learners test for the correct platform.My PIN won’t work?If your PIN is not working or you cannot remember it you can generate a new one. Generating a new PIN will mean that you will have to download the test package again.If you are a centre who book onscreen test using both QMA and Edexcel Online, you will have a different PIN for each system. If you are accessing your tests on both QMA and POP Assessor Dashboard (tests booked on Edexcel Online), this could cause you to get an Invalid PIN message when using the test player.To avoid this you can use the POP Assessor Dashboard to access all of your on-screen tests booked on both QMA and Edexcel Online in one place, meaning that you will only need to use the PIN that you have for the POP Assessor Dashboard.I’ve forgotten my password/cannot log inIf you have forgotten your password or are not able to log in please contact your account specialist who will be able to reset your password for you and check for anything else that could be preventing you from logging in.。
生产性实训车间项目化教学探索与实践
生产性实训车间项目化教学探索与实践作者：张国平王玉梅来源：《中国教育技术装备》2012年第21期摘要在电机与电气控制理实一体化教程的教学实践中，以“电气控制设备生产性实训车间”为教学平台，将本课程分解序化为具体的工程项目型学习任务，并以此为抓手进行项目化教学探索。
引入企业的真实机电设备产品及系统控制任务到教学中，让学生利用车间的软硬件资源及网络资源学会自主学习。
在完成工业电气控制柜系统设计与工艺实施过程中提高学生的创新思维能力、动手能力、综合分析能力，在开放的学习环境中享受自主学习带来的快乐。
关键词生产性实训车间；项目化教学；系统设计中图分类号：G712 文献标识码：B 文章编号：1671-489X(2012)21-0062-03Research and Practice of Projects-based Teaching in Productive Training Workshop//Zhang Guoping, Wang YumeiAbstract In the teaching practice o f “motor and electrical control theory and practice tutorial”, an “electrical control equipment training workshop” was used as teaching platform and the tutorial was decomposed into specific tasks, which is the starting point to explore project-based teaching. Real corporate mechanical, electrical equipment products and system control tasks were introduced into teaching, which enable students to learn how to self-learning based on workshop’s hardware, software and network resources. In the process of Industrial electrical control cabinet system design and implementation, students could improve ability of practice, innovation, analysis and enjoy self-learning in an open learning environment.Key words productive training workshop; project-based teaching; system design Author’s address Shihezi Vocational and Technical College, Shihezi, Xinjiang, China 8320002011年9月，笔者担任石河子职业技术学院机电103班（42人）电机与电气控制理实一体化教程的教学任务，教学时间为14周共288节课。
学生以自己为中心的例子英语作文
学生以自己为中心的例子英语作文全文共3篇示例，供读者参考篇1Students as the Center of Their Own EducationIn recent years, there has been a growing emphasis on student-centered learning in education. This approach to teaching and learning places the student at the center of their own education, empowering them to take control of their learning and become active participants in the learning process. By shifting the focus from the teacher to the student,student-centered learning has the potential to foster greater engagement, motivation, and ownership of learning outcomes.One example of student-centered learning in action is project-based learning. In this approach, students work onreal-world projects that require them to apply knowledge and skills from various subjects. For instance, a group of students might work on a project to create a community garden, which would involve researching and planning the garden layout, learning about plant biology and soil composition, and working together to build and maintain the garden. By working on aproject that has real-world relevance and meaning, students are able to see the practical applications of their learning and become actively engaged in the learning process.Another example of student-centered learning is personalized learning. This approach involves tailoring instruction to meet the individual needs and interests of each student. For example, a student who excels in math but struggles with reading might receive additional support and resources to improve their reading skills, while still being challenged in math at their own pace. By catering to the unique needs of each student, personalized learning can help students reach their full potential and achieve academic success.In addition to project-based and personalized learning, there are many other ways in which students can be at the center of their own education. For instance, students can take on leadership roles in the classroom, such as leading discussions, guiding group projects, or organizing school events. They can also be involved in setting goals and tracking their own progress, reflecting on their learning experiences, and providing feedback on their teachers and the curriculum.By embracing student-centered learning, educators can create a more student-centered and engaging learningenvironment that empowers students to take ownership of their own education. This approach not only fosters deeper learning and critical thinking skills, but also helps students develop important life skills such as self-motivation, collaboration, and problem-solving. Ultimately, by putting students at the center of their education, we can help them become lifelong learners who are prepared to succeed in the 21st century.篇2Being a student-centered essay, I would like to share an example of how a student took control of their own learning and achieved success.One student that comes to mind is Sarah, a high school sophomore who was struggling in her math class. Despite attending regular classes and completing homework assignments, Sarah found herself falling behind and feeling lost in the subject.Determined to turn things around, Sarah decided to take matters into her own hands. She started by seeking out extra help from her teacher after school hours. She would ask questions about concepts she didn't understand, work throughpractice problems, and seek clarification on any areas of confusion.In addition to seeking help from her teacher, Sarah also took advantage of online resources and tutoring services. She found instructional videos and practice problems that helped reinforce the material covered in class. She also attended a math tutoring center where she could receive personalized assistance and guidance.Furthermore, Sarah made a conscious effort to form study groups with classmates who were also struggling in math. They would meet regularly to review material, work on practice problems together, and provide support and encouragement to one another.Through her hard work and determination, Sarah was able to improve her understanding of math concepts and boost her confidence in the subject. By taking control of her own learning and seeking out the resources and support she needed, Sarah was able to turn her academic performance around and ultimately excel in her math class.This example serves as a reminder of the power of being a student-centered learner. By taking ownership of their education, seeking out help and resources, and actively engaging in theirlearning process, students like Sarah can achieve great success and reach their full potential.篇3Students always play a crucial role in the teaching and learning process. In recent years, there has been a growing emphasis on student-centered learning, where students take charge of their own education and actively participate in their learning process. This approach empowers students to become independent learners, critical thinkers, and problem solvers.One example of students taking charge of their education is through personalized learning. In a personalized learning environment, students have the opportunity to choose their own learning goals, pace, and pathway. For instance, students can select topics of interest to explore, set their own learning objectives, and design their own projects. By doing so, students become more engaged in their learning and take ownership of their education.Another example of students as the center of learning is through inquiry-based learning. In this approach, students are encouraged to ask questions, seek answers, and solve problems through investigation and exploration. For example, in a scienceclass, students may conduct experiments, collect data, analyze results, and draw conclusions based on their findings. By engaging in inquiry-based learning, students develop critical thinking skills, collaboration abilities, and a deeper understanding of the subject matter.Furthermore, collaborative learning is another example of students taking control of their education. In collaborative learning, students work together in groups to solve problems, complete projects, and share ideas. For instance, students can engage in discussions, debate ideas, offer feedback, and collaborate on shared tasks. By working in groups, students enhance their communication skills, teamwork abilities, and social awareness, which are essential for success in the 21st century.Overall, students as the center of learning is a powerful approach that promotes student engagement, motivation, and achievement. By empowering students to take charge of their education, teachers can foster a lifelong love of learning, instill a sense of responsibility, and nurture independent thinkers who are well-equipped to succeed in the future. Thus, it is essential for educators to embrace student-centered learning and createopportunities for students to thrive and excel in their academic journey.。
学生为中心的教育英语作文
Education that is centered around the student,often referred to as studentcentered education,is an approach that prioritizes the needs,interests,and learning styles of the students.This method contrasts with traditional teachercentered education,where the teacher is the primary source of knowledge and the students are expected to follow a set curriculum without much input from them.Key Features of StudentCentered Education:1.Engagement:In studentcentered classrooms,students are actively engaged in their learning.They participate in discussions,ask questions,and share their thoughts and ideas.2.Personalization:Learning is tailored to meet the individual needs of each student.This could involve differentiated instruction,where the teacher adjusts the content,process, and product to cater to the varying abilities and interests of students.3.Collaboration:Students often work together in groups,fostering a sense of community and encouraging peertopeer learning.This collaborative approach helps students to develop communication and teamwork skills.4.InquiryBased Learning:Instead of being passive recipients of knowledge,students are encouraged to take an active role in their learning by asking questions,conducting research,and solving problems.5.Choice and Autonomy:Students are given choices in what and how they learn.This could involve selecting projects or topics that interest them,or choosing the methods by which they demonstrate their understanding.6.Reflection:Students are encouraged to reflect on their learning experiences,which helps them to understand their own learning processes and to develop selfawareness.7.Feedback:Constructive feedback is provided to students in a timely manner,helping them to understand their strengths and areas for improvement.8.Technology Integration:Modern educational technology is often used to enhance learning experiences,making education more interactive and accessible.Benefits of StudentCentered Education:Increased Motivation:When students are engaged in topics that interest them,they aremore likely to be motivated to learn.Improved Critical Thinking:Inquirybased learning promotes the development of critical thinking skills as students analyze information and draw conclusions.Enhanced Retention:Personalized learning can lead to better retention of information because it is more relevant to the students interests and needs.Greater SelfEfficacy:As students take control of their learning,they develop a stronger sense of selfefficacy and confidence in their abilities.Development of Lifelong Learning Skills:By learning to take charge of their education, students are better prepared for lifelong learning and adaptability in a rapidly changing world.Challenges of Implementing StudentCentered Education:Resource Intensity:Personalizing education can be resourceintensive,requiring more time and effort from teachers to plan and adapt lessons.Assessment Complexity:Assessing student learning in a studentcentered environment can be more complex due to the diversity of learning paths and outcomes.Teacher Training:Teachers may need additional training to effectively implement studentcentered teaching strategies.In conclusion,studentcentered education aims to create a learning environment where students are at the heart of the educational process.By focusing on the individual needs and interests of students,this approach can lead to more engaged,motivated learners who are better equipped to succeed in their academic and future endeavors.。
列车运行控制系统的五个级别
列车运行控制系统的五个级别一、列车运行控制系统的五个级别列车运行控制系统是保障列车安全运行的重要设备，它通过控制列车的速度、位置和运行模式，确保列车在轨道上的稳定运行。
根据功能和安全性等方面的不同，列车运行控制系统可以分为五个级别，分别是ATC、ATO、CBTC、CTBC和ETCS。
二、ATC（Automatic Train Control）级别ATC是列车运行控制系统的最基本级别，它主要通过信号系统和车载设备实现对列车的自动控制。
在ATC级别下，列车通过接收信号系统发出的信息，控制列车的速度和位置，以确保列车在规定的区间内安全运行。
ATC级别适用于高速铁路等需要保证列车安全运行的场所。
三、ATO（Automatic Train Operation）级别ATO是在ATC基础上进一步发展的列车运行控制系统级别。
ATO级别在保证列车安全运行的基础上，更加注重列车的运行效率和准点性。
相比于ATC级别，ATO级别的列车运行更加自动化，列车的运行速度和位置更加精确可控。
ATO级别适用于城市轨道交通等高密度、高频率的线路。
四、CBTC（Communications-Based Train Control）级别CBTC是一种基于通信技术的列车运行控制系统级别，它通过车载设备和地面设备之间的通信，实现对列车的精确控制。
CBTC级别不仅可以控制列车的速度和位置，还可以实现列车的精确停站、车辆调度和列车间的安全距离控制等功能。
CBTC级别适用于复杂的轨道交通系统，如地铁、轻轨等。
五、CTBC（Communication-Based Train Control）级别CTBC是一种基于通信技术的列车运行控制系统级别，它在CBTC的基础上进一步发展，主要用于高速铁路系统。
CTBC级别通过车载设备和地面设备之间的通信，实现列车的精确控制和列车间的安全距离控制。
CTBC级别的列车运行更加高效、精确和安全，适用于高速铁路等需要高速、高频的线路。
迭代学习控制系统仿真及其PID参数整定
摘要本文首先分析了迭代学习控制的特点及其应用场合：迭代学习控制(Iterative Learning Control简称ILC)是智能控制理论的一个重要分支，其结构简单，跟踪效果好，不需要模型，或对模型的先验知识要求不高。
对诸如：具有非线性、强耦合、难建模且对控制精度要求高的机器人、注塑等周期性运动或间歇重复生产过程的对象，迭代学习控制的研究具有重要的意义。
其次，基于Matlab软件，进行了迭代学习控制系统仿真实验。
以注塑机生产过程为背景，进行了注射速度迭代学习控制实际应用仿真；最后，对离散系统的迭代学习控制系统进行了PID参数的优化、对迭代学习控制系统的学习律PID参数整定问题进行了研究，探讨了两种整定方法：利用广义逆阵拟合PID参数、利用神经网络拟合PID参数。
关键词:迭代学习控制；注塑机；注射速度；PID参数整定ABSTRACTThis paper first analyzes the characteristics of iterative learning control and its applications：iterative learning control(ILC) is an important branch of the intelligent control．Its main strategies are simple structure，perfect trajectory tracking，less prior knowledge of model．The research of ILC is significant for plants which are nonlinear, strong coupling, difficult to model，and the remand of high speed and high accuracy for motion control．such as robotic manipulators and so on．Secondly based on the Matlab software, do some simulation work about ILC and injection ram velocity of injection modeling machine; Finally study the tuning of iterative learning controller PID parameters using linear discrete-time systems and PID parameter tuning law of ILC, we use two methods: the generalized inverse matrix and neural network to fitting PID parameters.Keywords: iterative learning control；injection ram velocity；injection modeling machine；PID parameters tuning目录第一章前言 (1)1.1课题研究的背景 (1)1.2注塑机注射速度问题的研究 (1)1.3PID参数整定 (1)1.4课题研究的意义 (1)1.5本文研究的主要内容 (1)第二章迭代学习控制研究 (3)2.1迭代学习控制综述 (3)2.2.迭代学习控制研究现状 (5)2.3迭代学习控制仿真研究 (6)2.3.1 开环迭代学习控制 (6)2.3.2 闭环迭代学习控制 (8)2.4小结 (14)第三章注塑机注射速度仿真研究 (15)3.1注塑机控制 (15)3.1.1 注射阶段 (16)3.1.2 保压阶段 (17)3.1.3 预塑阶段 (17)3.2注射速度数学模型 (18)3.2.1 伺服阀 (18)3.2.2 阀控缸 (19)3.2.3 注射速度模型 (20)3.3注射速度迭代学习控制 (21)第四章迭代学习控制与PID参数整定 (23)4.1PID型离散系统迭代学习控制器参数的优化设计 (23)4.2基于迭代学习控制的PID参数整定 (29)4.2.1 利用广义逆阵拟合PID参数 (29)4.2.2 利用神经网络拟合PID参数 (32)第五章结论 (35)致谢 (36)参考文献 (37)第一章前言1.1 课题研究的背景1984年，Arimoto等人提出了迭代学习控制的概念，迭代学习控制 (I LC，iterative learning control是智能控制中具有严格数学描述的一个分支[1]。
英语原文
feedback abaptive robust precision motion controlof linear motorsLI Xu,Bin Yao*school of Mechanical Engineering Purversity,12588Mechanical Engineering Build ing,West Lafayette,A Received2February 2000;revised12October in final form19January2001An output feedback abaptive robust controller is constructed for precision motion control of linear motor drive systems.The control lao theoretically achieves a guaranteed high tracking accuracy for high-accelerayion/high-speed movements,as verified through experiments also.AbstractThis paper studies high performance robust motion control of linear motors that have a negligible electrlcal dynamics.A discon-tinuous projection based abapeive robust controller(ARC)is constructde Since only ontput signalis available for measurement,an observer is first designed to provide cxponentially conergent estimates of the unmessurable states.This observer has an extended filter structure so that on-line parameter abaptaion can be utilized to rebuec the effect of the possible large nominal disturbances.Estimation errors that come from initial state estimates and uncompensated disturbances are effectivelt dealt with via certaen robustfeedback at each step of the ARCbacdstepping design.Ghe resulting controller achieves a guaranteed output trackinh transientperformance and a prescribed final tracking accuracy.In the presence of parametrec umcertainties only,asymptotic output tracking is also achieved.The scheme is implemented on a precision epoxy core linear motor.Experimental results are pressnted to illustrate the effectiveness and the achieable control performance of the proposed.(c)2001Elsevier Science Ltd.All rights reserved.1.IntroductionModern mechanical systems,such as machine tools.semicond uctorepuipment,and automatic inspection machines,often require high-speed,high-accuracy linesr motions.These linear motions are usuallyrealized using rotary motors with mechanical transmission mechanisms such as reducton gears and lead screw.Such mechanical reansmissinos not only significantly reduce linear motion speed and dynamic response,but also introduce backlash,large frictional and indrial loads,and structural flexibility.Back lash and control aystem can achieve,As an alternatine,d irect drive linear motors.which elimiante the use of mechanical transmissions,positioning systems.Direct drive linear motor sysyems gain high-speed,hihg-accuracy potential by eliminating mechanical transmissons.However,they also lose the advantage of usingmechanical transmission-gear reduction reduces the effect of model uncertaintes such as parameter variations(e.g.uncertain payloads)and external disturbance (e.h.cutting forces in machining).Furthermore,certain types of linear motors(e.h.iron core linear motors)are subjected to significant force repple(Braembussche,Swevers,Van Brussel.&Vanherck,1996).These uncertain nonlinearties are directly transmittde to the load and have sinificant effects on the motion of the load.Thus,in order for a linear motor system to be able to function and to delive its high performance potential,a controllerwhich can achivev the required high accuracy in spite of various parametric uncetainties and uncertain nonlinear effects,has to be employed.Agreat deal of effort has been devotde to solving the diffculties in controllingt linear motor systems(Braembussche et al,1996;Alter&Tsao,1996,1994;Komada,Ishida,Ohnishi,&Horl,1991;Ehami&Tsuchiya ,1995;Otten,Vries,Amerongen,Randers,&Randers,&Gaal,1997;Yao&Xu1999).Alter and Tsao(1996)Presented a comperhensine design approach for the control of linear-motord riven machine tool aces.H∞optimal feedback control was used to provide high dynamic stifness to external disturbances(e.g,cutting forces in machining).Feedforwlrd was also introduced in Alter and Tsao(1994)to improve tracking performance.Practically,H∞design may be conservative for high-speed/high-accuracy tracking control and there is no systematic way to translate practicalabout plant uncertainty and modeling inaccuracy into quantitative terms that allow the application of H∞techniques.In Komada et al.(1991),a disturbance compensation method based ondisturbance observer(DOB)(Ohnishi,Shibata,&Murakami,1996)was proposed to made a linear motor system robust to model uncertainties,It was shown bwth theoretically and experimentally by Yao,Al-Majed,and Tomizuka(1997)that DOB design may not handle discontinuous disturbances such as Coulomb friction well and cannot deal with large extent of parametric uncertainties.To reduce the nonlinear effect of force ripple,in Braembussche et al.(1996),feedforward compensation terms,which were based on an off-line experimentally identified force riple model,were added to a positin terms,which were based on an off-line experimentally idntified force ripple model,were added to a position controller.Shnce not all magnets in a linesr motor and not all linear motors of the same type are identical,feedforward compensation based on the off-line edentified model may be too sensitive and costly to be useful.InOtten et al.(1997),a neural-netword-based learning feedforward controller was proposed to reduce positionalinaccuracy due to reproducible rippel forces or any other reproducible and slowly varying disturbances over different runs of the same desirde trajectory(or repetitive tasds).However,overall closed-loop stability was not huaranteed.In fact,it was observed in Otten al.(1997)that instability may occur at hihg-speed movements.Furthermore,the learning process may tade too long to be useful due to the use of a small adaptation rate for stability.In Yao and Xu(1999),under the assumption that the full state of the system is measured,the idea of abaptive robust control(ARC)(Yao&Tomizuda,1996,1997b)was generalixde to provide a theoretic frameword for the high performance motion control of and iron core linear motor.The controller tood into account the effect of model uncertainties coming from the inertia load,friction,force ripple and electrical parameters,etc.In particular,basde on the structuer of the motor mawdel,on-line parameter abaptation was utilizde to reduce the effect of parametric uncertainties while the uncompensated uncertain nonlinearities were handled effectively via certain robust control laws for high preformance.As a result,time,-consuming and costly rigorus offine identification of friction and ripplewas avoded without sacrificing tracding performance.In Xu and Yao(2000a,b),the proposed ARCalgorithm(Yao&Xu,1999)was applide on an epoxy core linear motor.To reduce the effect of measuerment noese,a desired compensationARCligorithm in which theregressor was calculatde by reference trajectory information was also presented and implemtnted.The ARCschemes in Xu and Yao(2000a,b)used velocity feedback.However,most linesr motors systems do not equip velocity sensors due to their special syructuer.In peratice,the velocity signal is useally obtained by the bacdkward difference of the positio n signal,which is very noisy and limits the overall rerformance.It is thus of practical significance to see if one can construct ARC controllers based on the postion measurement only,which is the tocus fo the paper.An output feedbacd ARC scheme is constructed for a linear motor subjected to both parametric uncertainties and bounded disturbances.Since only the ortput sihnal is available for measurement,a Kerisselmeier observer(Kerisselmeier,1997)is first designed to provide exponentially convergent estimates of the unmeasurable syates.This observerhas an extended filter structuer so that on-line parameter abaptation can be utilized to reduce the erffect of the possible large nominal disturbance,which is very important from the vieo point of application(Yao et al,1997).The destabilizing effect of the estimation errors is effectively dealt with using robust feedback at cach step of the design procedure.The resultintg controller chieres a guaranteed transient performance and a prescribed final tracking accuracy.In the pressene of parametric uncertainties only,asmptotic output is also achieved.Finally, the proposed scheme,as well as a PID controller,is implemented on an epoxy core linear parative experimental ersults are presented to justify the validity of the ARC algorithm.The paper is organized as follows.Problem formulation and dynamic models are presentad in Section2.The proposed ARCcontroller is shown in Section 3.Experimental tetup and comparative experimental results are persented in Section4.and conclusions are drawn in Section5.formulation and dynamic modelsThe linear motor considered here is a current-controlled three-phase epoxy core motor driving a linear positioning stage supported by recerculating bearings.To fulfill the high performance repuirements,the model is obtained to include most nonlinear effects like friction and force ripple.In the derivation fo the model,the current dynamics is neglected in comparison to the mechanical dynamics due to the much faster electric response.The mathematical model of the system can be descrebed by the following equations:Mq=u-F（·,q），F(q,q)=Ff+Fr-Fd(1)where q(t),represent the position,velocity and accleeration of the inertia loab,respectinelt,M is the normalized mass of the inertia load plus the coil assembly u is the input voltage to the motor,F is the mormalized lmped effect of uncertain nonlieartites such as friction Ff ripple force Fr and external disturbance Fd(e.g,cutting force in cachining).While there have been many friction models proposed(Armstrong-Helouvry,Dupont,&Canudas de Wit,1994),a simple and often adequqte approach is to regard the friction force as a ststic nonlinear function fo the velocity,i.e,Ff(q),which is ginen byFf(q)=Bq+Ffn(q)(2)where Be is the iquivalent viscous coeffictent of the system,Ffn is the monlinear firction term that can be modeled as(Armstrongt-Helouvry et al,1994)Ffn(q)=-[fc+(fs-fc)e-(q/qs)ζ]sgn(q)(3)where fs is the level of stiction,fc is the livel of Coulomb friction,and qs andζare empirical parameters used to describe the Stribeck effect.Thus,considering(2).one can rewrite(1)asMq=u-Bq-Ff(n)(q)+△(4)where△=Fd-Frrepresents the lumped disturbanec.Let qr(t)be the reference motion trajectory,which is assumbd to be known,bounded with bounded derivatives up to the second order.The tontrol objectinve is to synthesiz e a control input u such thatoutput q(t)tracks qr(t)as coselt as possible in spite of variuos model uncertainties 3.Adaptive robust control fo linear motor systems3.1Friction compensationA simple but effectime method for overcoming problemsd ue to friction is to introduce a cancellation term for the friction force.Since the nonlinearity Ffn depends on te velocity q which is not measurable,the friction compensation scheme directlt to achieve our objective In order to bypass the difficulty,in the following,the "estimated friction force"Ffn(qd)will be used to approximate Ffn(q)where qd is the desired trajectory to be tracked by q The approximation error Ffn=Ffn(qd)-Ffn(q)will be treated as disturbance.In other words,the control input u(t)becomes(5)where u*is the output of an abaptine robust controller yet to be designed.Substituting(5)into(4),one obtainsMq=u*(t)+Bq+d(6)where d=△+FfnIn general,the system is subject to parametric uncertainties due to the variations of M,B,and the mominal value of the lumped disturbance d,dn Define the unknown parameter setθ=[θ1,θ2,θ3]asθ1=1/M,θ2=B/M,θ3=dn/M.A state space realization of the plant(6),which is linearly parameteriz ed in terms ofθ,is thus given byX1=X2-θ2X1X2=θlu*+θ3+dY=X1(7)where x1is one state of the second order system that represents the position q,x2is the othe state that is not measurable,y is the output,and d=(d-dn)/M.For simplicity,in the following,the following notations are used:for the ith component of the vecror·,·min for the minimum value of·,and·max for the maximum valus of·.The operation≤for two vectors is performed in terms of the correspond ing eld ments of the vectors.The following practical assumption is made:extent of the parametric uncertainties and uncertain nonlinearitise is known,i.e.(8)3.2State estimationSince only the output y is available for measurement,a nonlinesr observer is first built to provide an exponentially converhent estimate of the unmeasurable state x2.The design model(7)can be rewritten asx=AOx+(k-e1θ2)y+e2θ3+e2θ1u*+e2dy=x1(9)where x[x1,x2]t,e1and e2denote the standard bassis vectors in R2and(10)Then,ty suitably coosing k,the observer matrix Ao will be stable.Thus,there exists a symmetric positive definite(s.p.d)matrix Psuch that(11)Following the design procedure of Kestic,Kanelladopoulos,and Kokotovec(1995),one can define the following filters:ζ2=AOζ2+KYζ1=AOζ1-ely(12)v=AOv-e2u*Ψ=AOΨ+e2Notice that the last equation of(12)is intsoduced so that parameter abaptation can be used to reduce the parametric uncertainties coming fromθ3.The state estimates can thus be represented byx=ζ2-θ2ζ1+θlv+θ3Ψ(13)Lesεx=x-x1be the estimation error.from(9),(12)and(13),it can be verified that the observer error dynamics is given by(14)The solution of Eq(14)can be written asεx=ε+εu,whereεis the z ero input respones satisfyingε=Aoεandthe z ero state response.Noting Assumption1and the fact that matrix Ao isstable,one has(16)whereδ(t)is known.In the following controller derign,εandεu will be treated as disturbances and accounted for using different robust control functions at each step of the design to achieve a guaranteed final tracking accuracy.Remark1.Theζand v variables in(12)can be obtained from the algebraic expressions(Krstic et al.1995)ζ2=-AOηζ1=-AOη(17)v=λwhereηandλare the states fo the following two-dimensional filiersη=AOη+e2yr=aor+e2u*(18)3.3Paramerer projectionLesθdenote the estimate ofθandθthe estimateon error(i,eθ2=θ1-θ).In view of(8),the following adaptation law with discontinuous projection modification can be usedθ=Projθ(rt)(19)where r>0is a diagoal matrixτis an adaptation function to be synthesized later.The projection maqq ing Projθ(·)=[Projθ(·1),...,Projθp(·p)T is defined in Yao and Tomizuda(1996)and Sastry and Bodson(1989)as0ifθ1i=θimax and·i>0Projθt(·i)=0ifθ1i=θimax and·i<0(20)·i otherwiseIt can be shown(Yao and Tomizuka,1996)that for any adaptation functionτ,the projection mapping defined in(20)guaranteesP1θ∈Ωθ={θ:θmin≤θ≤θmax}P2θ{r Projθ(rt)-T}≤0(21)。
自动控制原理第八版英文版教学设计 (2)
Teaching Design for Automatic Control Principles 8thEdition (English Version)1. IntroductionAutomatic control principles are the foundation of modern control engineering. With the rising demand for control engineering talents, it is essential to design a suitable teaching plan for students who are interested in this field. This teaching design is med at providing an understanding of the basic concepts of automatic control principles and their applications.2. ObjectivesThe primary objectives of the teaching design are:•To equip students with knowledge, concepts, and analytical skills required in the automatic control principles field.•To facilitate students with the application of control theory methods to the design and analysis of control systems.•To establish a foundation for further study in advanced control techniques.3. Teaching MethodologyThe teaching of automatic control principles will use the flipped classroom approach. This approach will enable students to engage in a more interactive and practical learning experience where they can applythe theoretical principles they have learned in class to real-world applications.3.1 Pre-class LearningBefore class, students are expected to study the related learning materials, which includes studying the assigned chapters from the textbook – Automatic Control Principles, 8th edition, written by the renowned author, G. Franklin, J. Da Powell, and A. Emami-Naeini.3.2 In-class LearningClassroom lectures will not be used to cover the fundamental concepts of automatic control principles. Rather, the students will participate in interactive sessions, where they will have a chance to engage in problem-based learning activities.3.3 After-class LearningAfter class, students will have access to online resources such as recorded classroom sessions, tutorials, and online assessments. The materials will be made avlable to students via the school’s online learning management system.4. Course OutlineThe course outline will cover the following key topics:•Introduction to automatic control principles and its applications.•Mathematical modeling of dynamic systems.•Time-domn analysis of control systems.•Root-locus analysis of control systems.•Frequency-domn analysis of control systems.•Design of classical control systems.•Introduction to modern control principles.•Digital control systems.5. Teaching Plan•Week 1: Introduction to Automatic Control Principles•Week 2-3: Mathematical Modeling of Dynamic Systems•Week 4-5: Time-domn Analysis of Control Systems•Week 6-7: Root-locus Analysis of Control Systems•Week 8-9: Frequency-domn Analysis of Control Systems•Week 10-11: Design of Classical Control Systems•Week 12-13: Introduction to Modern Control Principles•Week 14-15: Digital Control Systems6. Course AssessmentCourse assessment will be divided into formative and summative assessments. The formative assessments will include online quizzes, homework assignments, and problem-solving in the classroom activities, while the summative assessment will include a final examination.7. ConclusionIn conclusion, this teaching design is med at equipping studentswith theoretical concepts and analytical skills required in the automatic control principles field. The teaching will be organized using the flipped classroom approach, which will be more interactive and practical for students to participate in problem-based learningactivities. With this approach, students will have an opportunity to apply the theoretical principles they have learned in class to real-life problems. The course ms to establish a strong foundation for further study in advanced control techniques.。
基于深度强化学习的固高直线一级倒立摆控制实验设计
基于深度强化学习的固高直线一级倒立摆控制实验设计冯肖雪 谢天 温岳 李位星(北京理工大学 自动化学院 北京 100086)摘要： 为适应各高校人工智能专业学生对于机器学习领域的学习需求，同时兼顾固高科技直线一级倒立摆控制系统可操作性、实时性和安全性，设计了一套基于深度强化学习的固高直线一级倒立摆控制实验方案。
首先采用深度强化学习算法的无模型控制结构搭建控制器并进行虚拟仿真实验。
考虑倒立摆电机驱动刷新频率的限制以及提高样本处理速度，进一步设计了基于离线Q 学习算法的平衡控制器实现倒立摆实物稳定控制。
该实验方案既加深了学生对人工智能领域知识的理解，也适应了固高科技直线一级倒立摆的应用场景。
关键词： 直线一级倒立摆 深度强化学习 Deep Q Network 算法 Q 学习算法中图分类号： TP319文献标识码： A文章编号： 1672-3791(2023)23-0004-07Experimental Design of Googol's Linear Single Inverted PendulumControl Based on Deep Reinforcement LearningFENG Xiaoxue XIE Tian WEN Yue LI Weixing(School of Automation, Beijing Institute of Technology, Beijing, 100086 China)Abstract: In order to meet the learning needs of students majoring in artificial intelligence in colleges and universi‐ties in the field of machine learning, and take into account the operability, instantaneity and safety of the linear single inverted pendulum control system of Googol Tech, this paper designs an experimental plan for Googol's linear single inverted pendulum control based on deep reinforcement learning. Firstly, this paper uses a model-free control structure of the deep reinforcement learning algorithm to construct a controller and conduct virtual simulation ex‐periments. Considering the limitation of the refresh frequency driven by the inverted pendulum motor and the im‐provement of sample processing speed, it further designs a balance controller based on the offline Q-Learning algo‐rithm to achieve the physical stability control of the inverted pendulum. This experimental plan not only enhances studnets' understanding of the knowledge in the field of artificial intelligence, but also adapts to the application sce‐nario of the linear single inverted pendulum of Googol Tech.Key Words: Linear single inverted pendulum; Deep reinforcement learning; DQN algorithm; Q-Learning algorithm倒立摆控制系统是一种典型的高阶次、非线性、多变量、强耦合的自不稳定系统。
abac权限模型原理
abac权限模型原理
ABAC（Attribute-Based Access Control）权限模型是一种基于属性的访问控制模型，其原理是通过动态计算一个或一组属性是否满足某种机制来进行授权判断。
ABAC模型不同于传统的访问控制模型，如RBAC（Role-Based Access Control）等，它并不是将用户直接关联到权限，而是基于实体（如用户、资源等）的属性来进行权限控制。
在ABAC模型中，权限控制是基于一系列属性来进行的，这些属性通常包括用户属性、环境属性、操作属性和对象属性等。
用户属性可以是用户的年龄、性别、角色等；环境属性可以是时间、地点、设备等；操作属性可以是读、写、执行等；对象属性可以是文件的类型、大小等。
当需要进行权限判断时，ABAC模型会根据这些属性的值来计算是否满足某种条件，从而确定用户是否有权对某个对象进行某种操作。
这种计算过程可以编写成逻辑表达式，从而实现非常灵活的权限控制。
ABAC模型的一个主要优点是它可以很好地解决RBAC等模型的缺点，特别是在新增资源时容易维护。
由于权限控制是基于属性进行的，因此当新增资源时，只需要为这些资源定义相应的属性，而不需要重新配置用户的角色或权限。
然而，ABAC模型的缺点也很明显，它的规则复杂，不易看出主体与客体之间的关系，实现起来也比较困难。
因此，在实际应用中，需要根据具体情况来选择适合的访问控制模型。
英语四级试卷模拟考试
英语四级试卷模拟考试一、写作（15%）题目： The Importance of Reading Classics。
要求：1. 阐述阅读经典著作的重要性；2. 给出你对阅读经典著作的建议；3. 字数不少于120字，不多于180字。
二、听力理解（35%）Section A.Directions: In this section, you will hear 8 short conversations and 2 long conversations. At the end of each conversation, one or more questions will be asked about what was said. Both the conversation and the questions will be spoken only once. After each question there will be a pause. During the pause, you must read the four choices marked A), B), C) and D), and decide which is the best answer.Short Conversations (1 - 8)1. A) At a bookstore.B) At a library.C) At a supermarket.D) At a post office.Question: Where does the conversation most probably take place?2. A) He is a teacher.B) He is a doctor.C) He is a lawyer.D) He is a businessman.Question: What does the man do?3. A) She likes the movie very much.B) She doesn't like the movie at all.C) She thinks the movie is just so - so.D) She hasn't seen the movie yet.Question: What does the woman think of the movie?4. A) Go to the park.B) Go to the cinema.C) Stay at home.D) Do some shopping.Question: What are they going to do?5. A) 8:00.B) 8:15.C) 8:30.D) 8:45.Question: What time is it now?6. A) By car.B) By bus.C) By train.D) By plane.Question: How will they go to Beijing?7. A) Red.B) Blue.C) Green.D) Yellow.Question: What color does the woman like best?8. A) Husband and wife.B) Father and daughter.C) Teacher and student.D) Boss and employee.Question: What's the relationship between the two speakers?Long Conversations (9 - 15)Conversation 1.Questions 9 - 11 are based on the conversation you have just heard.9. A) She wants to find a part - time job.B) She wants to travel around the world.C) She wants to study abroad.D) She wants to start her own business.Question: What does the woman want to do?10. A) Her parents.B) Her friends.C) Her teachers.D) Her classmates.Question: Who can give her some advice?11. A) This weekend.B) Next week.C) Next month.D) Next year.Question: When will she make a decision?Conversation 2.Questions 12 - 15 are based on the conversation you have just heard.12. A) It's too expensive.B) It's too big.C) It's too small.D) It's too far from her office.Question: What's the problem with the apartment?13. A) 1000 yuan.B) 1200 yuan.C) 1500 yuan.D) 1800 yuan.Question: How much is the rent?14. A) One.B) Two.C) Three.D) Four.Question: How many rooms are there in the apartment?15. A) She will rent it.B) She will think about it.C) She will look for another apartment.D) She will buy an apartment.Question: What will the woman do?Section B.Directions: In this section, you will hear 3 short passages. At the end of each passage, you will hear some questions. Both the passage and the questions will be spoken only once. After each question there will be a pause. During the pause, you must read the four choices marked A), B), C) and D), and decide which is the best answer.Passage 1.Questions 16 - 18 are based on the passage you have just heard.16. A) In 1990.B) In 1995.C) In 2000.D) In 2005.Question: When was the company founded?17. A) Computers.B) Mobile phones.C) Cars.D) Clothes.Question: What does the company produce?18. A) In Asia.B) In Europe.C) In America.D) In Africa.Question: Where is the company's main market?Passage 2.Questions 19 - 21 are based on the passage you have just heard.19. A) Reading.B) Writing.C) Speaking.D) Listening.Question: Which skill is the most important in language learning?20. A) By reading a lot of books.B) By watching English movies.C) By listening to English songs.D) By talking with native speakers.Question: How can one improve their speaking skills?21. A) Once a week.B) Twice a week.C) Three times a week.D) Every day.Question: How often should one practice speaking?Passage 3.Questions 22 - 25 are based on the passage you have just heard.22. A) To make friends.B) To get information.C) To kill time.D) To do business.Question: Why do people use the Internet?23. A) Chatting.B) Shopping.C) Studying.D) Working.Question: Which is the most popular activity on the Internet?24. A) It's convenient.B) It's cheap.C) It's interesting.D) It's safe.Question: What's the advantage of online shopping?25. A) They are worried about the security.B) They don't like shopping online.C) They don't know how to use the Internet.D) They prefer to go to the real stores.Question: Why do some people not like online shopping?Section C.Directions: In this section, you will hear a passage three times. When the passage is read for the first time, you should listen carefully for its general idea. When the passage is read for the second time, you are required to fill in the blanks with the exact words you have just heard.When the passage is read for the third time, you should check what you have written.Passage.The Internet has become an important part of our daily lives. We can use it to do many things, such as getting information, _(26)_, chatting with friends, and so on. However, the Internet also has some _(27)_. For example, some people may use it to spread false information or _(28)_. So we should use the Internet _(29)_ and be careful not to be _(30)_ by the false information.三、阅读理解（35%）Section A.Directions: In this section, there is a passage with ten blanks. You are required to select one word for each blank from a list of choices given in a word bank following the passage. Read the passage through carefully before making your choices. Each choice in the word bank is identified by a letter. Please mark the corresponding letter for each item on Answer Sheet 2. You may not use any of the words in the word bank more than once.Questions 31 - 40 are based on the following passage.The development of modern technology has brought great _(31)_ to our lives. For example, we can use mobile phones to communicate with others_(32)_ no matter where they are. We can also use the Internet to get a large amount of information in a very short time. However, modern technology also has some _(33)_. For example, some people are so _(34)_ on mobile phones that they ignore the people around them. And the over - use of the Internet may also cause some _(35)_ problems, such as information overload and Internet addiction.So, we should make good use of modern technology and at the same time _(36)_ its negative effects. We should not let modern technology _(37)_ our lives, but use it to improve our quality of life. For example, we can set _(38)_ for using mobile phones and the Internet, and use them in a _(39)_ way. In addition, we should also encourage people to communicate face - to - face more often and _(40)_ the real world.Word Bank.A) benefits.B) addicted.C) easily.D) negative.E) control.F) limits.G) enjoy.H) psychological.I) replace.J) properly.Section B.Directions: In this section, you will read several passages. Each passage is followed by several questions based on its content. You are to answer these questions on the basis of what is stated or implied in the passage.Passage 1.Questions 41 - 45 are based on the following passage.The idea of having a single career has been an old - fashioned concept for quite some time now. People are increasingly choosing to have multiple careers throughout their lives. There are several reasons for this trend.First, the job market is constantly changing. New industries are emerging, and old ones are disappearing. This means that people may need to retrain and change their careers in order to stay employed. For example,with the rise of the digital age, many people who used to work intraditional print media have had to learn new skills in order to work in online media.Second, people are living longer and healthier lives. This gives them more time to pursue different interests and careers. They may want to try something new after spending many years in one field. For example, a person who has worked as a doctor for 20 years may decide to study art and become an artist in their later years.Finally, having multiple careers can be more fulfilling. It allows people to explore different aspects of their personalities and talents.They can gain different experiences and skills from each career, which can make them more well - rounded individuals.41. What is the old - fashioned concept mentioned in the passage?42. Why do people need to change their careers according to the passage?43. What is an example of the job market change given in the passage?44. How does living longer affect people's career choices?45. What are the benefits of having multiple careers?Passage 2.Questions 46 - 50 are based on the following passage.In recent years, the sharing economy has become a popular trend. The sharing economy refers to the economic model in which people share resources, such as cars, houses, and tools, through online platforms. There are several advantages of the sharing economy.First, it can save resources. For example, if people share cars, fewer cars will be needed, which can reduce the consumption of energy and raw materials. Second, it can be more cost - effective. For example, people can rent a house or a car at a lower price through sharing platforms than they would if they were to buy or rent them in the traditional way. Third, itcan also promote social interaction. When people share resources, they have the opportunity to meet and interact with other people.However, the sharing economy also has some challenges. One of the main challenges is the lack of regulation. Since the sharing economy is a relatively new concept, there are not enough laws and regulations to govern it. This can lead to problems such as safety concerns and unfair competition. Another challenge is the issue of trust. People need to trust the people they are sharing resources with and the platforms they are using.46. What is the sharing economy?47. What are the advantages of the sharing economy?48. What is the main challenge related to the lack of regulation in the sharing economy?49. Why is trust an issue in the sharing economy?50. Do you think the sharing economy will continue to grow in the future? Why or why not?Section C.Directions: There is one passage in this section. You are required to answer the questions below the passage according to what is stated or implied in the passage.Passage.The concept of "green living" has been around for a while, but it has become more important in recent years. Green living refers to a lifestyle that is environmentally friendly and sustainable. There are many ways to practice green living.One way is to reduce waste. We can do this by recycling, reusing, and reducing our consumption. For example, we can recycle paper, plastic, and glass products. We can also reuse items such as shopping bags and water bottles. And we can reduce our consumption of non - renewable resources such as oil and coal.Another way is to use renewable energy sources. We can install solar panels on our roofs to generate electricity. We can also use wind turbines in areas with strong winds. These renewable energy sources are clean and sustainable, and they can help reduce our dependence on fossil fuels.In addition, we can also choose environmentally friendly products. For example, we can choose products made from recycled materials or products that are biodegradable. We can also choose products that are produced in an environmentally friendly way, such as products that are made without using harmful chemicals.51. What is "green living"?52. How can we reduce waste?53. What are some renewable energy sources mentioned in the passage?54. What are the characteristics of renewable energy sources?55. How can we choose environmentally friendly products?四、翻译（15%）Part A.Directions: Translate the following sentences into English.1. 他是一个勤奋的学生，总是第一个到教室。
第1章 绪论
5
主要经历三个阶段： 经典控制理论 现代控制理论 智能控制理论
6
1.1.1 经典控制理论
1 自动装置的发明与应用
公 元 前 1500 年 ， 埃 及 人 （ Egyptians ） 和 巴 比 伦 人 （Babylonian.）发明了世界上最早的计时器－水钟，又称漏刻、 漏滴、漏壶或漏等。
7
图1-1 铜壶漏刻
Control
Systems》
Benjamin
高教出版社
6.《Modern
等
Control
Systems》
Richard C.Dorf 高教出版社
2
第一章
绪 论
主要内容：
1.1 引言
1.2
1.3 1.4 1.5 1.6
自动控制的基本概念
自动控制系统的组成 自动控制系统的分类 自动控制系统的应用实例 对自动控制系统的基本要求及教学内容
4
自动化的应用领域 ：
工农业生产（如压力、张力、温度、流量、位移、湿度、粘度等自动 控制） 国防建设（如飞机自动驾驶、火炮自动跟踪、导弹、卫星、宇宙飞船 等自动控制） 社会经济（如模拟经济管理过程、经济控制论、大系统、交通管理、 图书管理等） 人类生活（如生物控制论、波斯顿假肢、人造器官等）
8
1642年，法国物理学家帕斯卡（B. Pascal）发明了第一台机械式十 进制加法器，解决了自动进位这一关键问题，也第一次确立了计算器 的概念，因此他被公认为制造机械计算机的第一人。 1657年，荷兰科学家惠更斯（C. Huygens）应用伽利略（G. Galilei， 1564-1642）的理论设计了钟摆，在他的指导下年轻的钟匠考斯特（S.
同时，由于有反馈的存在，整个控制过程是闭合的，故也称为闭 环控制。
企业管理常用英文缩写
企业管理常用名词缩写作者：Christian5S : 5S管理ABC : 作业制成本制度(Activity-Based Costing)ABB : 实施作业制预算制度(Activity-Based Budgeting)ABM : 作业制成本管理(Activity-Base Management)APS : 先进规画与排程系统(Advanced Planning and Scheduling) ASP : 应用程式服务供应商（Application Service Provider）ATP : 可承诺量(Available To Promise)A VL : 认可的供应商清单(Approved Vendor List)BOM : 物料清单(Bill Of Material)BPR : 企业流程再造(Business Process Reengineering)BSC : 平衡记分卡(Balanced ScoreCard)BTF : 计划生产(Build To Forecast)BTO : 订单生产(Build To Order)CPM : 要径法(Critical Path Method)CPM : 每一百万个使用者会有几次抱怨(Complaint per Million) CRM : 客户关系管理(Customer Relationship Management)CRP : 产能需求规划(Capacity Requirements Planning)CTO : 客制化生产(Configuration To Order)DBR : 限制驱导式排程法(Drum-Buffer-Rope)DMT : 成熟度验证(Design Maturing Testing)DVT : 设计验证(Design Verification Testing)DRP : 运销资源计划(Distribution Resource Planning)DSS : 决策支援系统(Decision Support System)EC : 设计变更／工程变更(Engineer Change)EC : 电子商务(Electronic Commerce)ECRN : 原件规格更改通知(Engineer Change Request Notice)EDI : 电子资料交换(Electronic Data Interchange)EIS : 主管决策系统(Executive Information System)EMC : 电磁相容(Electric Magnetic Capability)EOQ : 基本经济订购量(Economic Order Quantity)ERP : 企业资源规划(Enterprise Resource Planning)FAE : 应用工程师(Field Application Engineer)FCST : 预估(Forecast)FMS : 弹性制造系统(Flexible Manufacture System)FQC : 成品品质管制(Finish or Final Quality Control)IPQC : 制程品质管制(In-Process Quality Control)IQC : 进料品质管制(Incoming Quality Control)ISO : 国际标准组织(International Organization for Standardization) ISAR : 首批样品认可(Initial Sample Approval Request)JIT : 即时管理(Just In Time)KM : 知识管理(Knowledge Management)L4L : 逐批订购法(Lot-for-Lot)LTC : 最小总成本法(Least Total Cost)LUC : 最小单位成本(Least Unit Cost)MES : 制造执行系统(Manufacturing Execution System)MO : 制令(Manufacture Order)MPS : 主生产排程(Master Production Schedule)MRO : 请修(购)单(Maintenance Repair Operation)MRP : 物料需求规划(Material Requirement Planning)MRPII : 制造资源计划(Manufacturing Resource Planning) NFCF : 更改预估量的通知Notice for Changing ForecastOEM : 委托代工(Original Equipment Manufacture)ODM : 委托设计与制造(Original Design & Manufacture) OLAP : 线上分析处理(On-Line Analytical Processing)OLTP : 线上交易处理(On-Line Transaction Processing)OPT : 最佳生产技术(Optimized Production Technology)OQC : 出货品质管制(Out-going Quality Control)PDCA : PDCA管理循环(Plan-Do-Check-Action)PDM : 产品资料管理系统(Product Data Management)PERT : 计画评核术(Program Evaluation and Review Technique) PO : 订单(Purchase Order)POH : 预估在手量(Product on Hand)PR : 采购申请Purchase RequestQA : 品质保证(Quality Assurance)QC : 品质管制(Quality Control)QCC : 品管圈(Quality Control Circle)QE : 品质工程(Quality Engineering)RCCP : 粗略产能规划(Rough Cut Capacity Planning)RMA : 退货验收Returned Material ApprovalROP : 再订购点(Re-Order Point)SCM : 供应链管理(Supply Chain Management)SFC : 现场控制(Shop Floor Control)SIS : 策略资讯系统(Strategic Information System)SO : 订单(Sales Order)SOR : 特殊订单需求(Special Order Request)SPC : 统计制程管制(Statistic Process Control)TOC : 限制理论(Theory of Constraints)TPM : 全面生产管理Total Production ManagementTQC : 全面品质管制(Total Quality Control)TQM : 全面品质管理(Total Quality Management)WIP : 在制品(Work In Process)5S管理5S是由日本企业研究出来的一种环境塑造方案，其目的在藉由整理(SEIRI)、整顿(SEITON)、清扫(SEISO)、清洁(SEIKETSU)及身美(SHITSUKE)五种行为来创造清洁、明朗、活泼化之环境，以提高效率、品质及顾客满意度。
model-based control 和 model predictive control -回复
model-based control 和model predictivecontrol -回复Model-based control和Model Predictive Control（MPC）是现代控制理论中两个重要的概念。
它们都基于系统的模型来实现对系统的控制，并在工业应用中得到广泛应用。
本文将逐步介绍Model-based control 和MPC的概念、原理、优缺点以及其在不同领域的应用。
一、Model-based control（基于模型的控制）Model-based control是一种基于系统模型来进行控制的方法。
它的基本思想是通过建立系统的数学模型，预测系统的响应，并根据预测结果实现控制。
Model-based control可以分为开环控制和闭环控制两种方式。
1. 开环控制：开环控制是指控制器输出信号仅根据输入信号进行决策，不考虑系统的状态或输出。
在开环控制中，系统的模型被用来预测系统的输出，并根据预测结果调整控制器的输出信号。
开环控制的一个典型应用是步进电机的控制，其中控制器根据输入信号控制电机的步进角度。
2. 闭环控制：闭环控制是指控制器输出信号不仅依赖于输入信号，还依赖于当前系统的状态或输出。
在闭环控制中，系统的模型被用来预测系统的响应，并根据响应调整控制器的输出信号。
闭环控制的一个典型应用是温度控制，其中控制器根据当前温度和设定值之间的误差来调整加热器的输出功率。
Model-based control的优点是可以提供对系统行为的准确预测，并且可以根据预测结果进行优化控制。
然而，它的缺点是对系统模型的准确性要求较高，且对于复杂系统的模型建立较为困难。
此外，Model-based control往往是一种离线计算的方法，所以在实时控制中可能需要进行一定的近似或简化。
二、Model Predictive Control（模型预测控制）Model Predictive Control是一种基于系统模型的高级控制方法。
基于外环速度补偿的封闭机器人确定学习控制
基于外环速度补偿的封闭机器人确定学习控制王 敏 1, 2林梓欣 1王 聪 3杨辰光1摘 要 针对未开放力矩控制接口的一类封闭机器人系统, 提出一种基于外环速度补偿的确定学习控制方案. 该控制方案考虑机器人受到未知动力学影响, 且具有未知内环比例积分(Proportional-integral, PI)速度控制器. 首先, 利用宽度径向基函数(Radial basis function, RBF)神经网络对封闭机器人的内部未知动态进行逼近, 设计外环自适应神经网络速度控制指令. 在实现封闭机器人稳定控制的基础上, 结合确定学习理论证明了宽度RBF 神经网络的学习能力, 提出基于确定学习的高精度速度控制指令. 该控制方案能够保证被控封闭机器人系统的所有信号最终一致有界且跟踪误差收敛于零的小邻域内. 在所提控制方案中, 通过引入外环补偿控制思想和宽度神经网络动态增量节点方式, 减小了设备计算负荷, 提高了速度控制下机器人的运动性能, 解决了市场上封闭机器人系统难以设计力矩控制的难题, 实现了不同工作任务下的高精度控制. 最后数值系统仿真结果和UR5机器人实验结果验证了该方案的有效性.关键词 确定学习, 速度补偿控制, 神经网络, 封闭机器人引用格式 王敏, 林梓欣, 王聪, 杨辰光. 基于外环速度补偿的封闭机器人确定学习控制. 自动化学报, 2023, 49(9): 1904−1914DOI 10.16383/j.aas.c220575Deterministic Learning of Manipulators With Closed ArchitectureBased on Outer-loop Speed Compensation ControlWANG Min 1, 2 LIN Zi-Xin 1 WANG Cong 3 YANG Chen-Guang 1Abstract In this paper, a deterministic learning outer-loop speed compensation control scheme is proposed for a class of manipulator systems with closed architecture and without open torque control interface. The proposed scheme focuses on that the manipulator is affected by unknown modelling dynamics and has an unknown inner-loop proportional-integral (PI) speed controller. Firstly, the broad radial basis function (RBF) neural network is used to approximate the internal unknown dynamics of the manipulator with closed architecture, and the outer-loop adapt-ive neural network speed control command is designed by using the Lyapunov function. Based on the stable control of man-ipulator with closed architecture, the dynamic learning ability of RBF neural network is verified, and then the high-accuracy speed control command is designed based on the deterministic learning theory. The proposed control sch-eme guarantees that all signals of the manipulator system with closed architecture are ultimately uniformly boun-ded, and the tracking error converges to a small neighborhood of zero. By the combination of outer-loop compensa-tion control and dynamic incremental node of broad neural networks, the proposed scheme reduces the computing lo-ad, improves the motion performance of the robot under speed control, solves the torque control design difficulty of the closed manipulator, and realizes high-precision control in different working tasks. Finally, simulation results of numerical system and experimental results of UR5 robot are used to show the effectiveness of the proposed scheme.Key words Deterministic learning, speed compensation control, neural network, manipulators with closed architec-tureCitation Wang Min, Lin Zi-Xin, Wang Cong, Yang Chen-Guang. Deterministic learning of manipulators with closed architecture based on outer-loop speed compensation control. Acta Automatica Sinica , 2023, 49(9):1904−1914近年来, 机器人在工程应用和日常生活中发挥着越来越重要的作用, 被广泛应用于空间探测、焊收稿日期 2022-07-14 录用日期 2023-01-11Manuscript received July 14, 2022; accepted January 11, 2023国家自然科学基金(62273156, 61890922, U20A20200, 61973129), 广东省自然科学基金(2019B151502058), 鹏城实验室重大攻关项目(PCL2021A09), 佛山市科技攻关项目(2020001006308,2020001006496)资助Supported by National Natural Science Foundation of China (62273156, 61890922, U20A20200, 61973129), Natural Science Foundation of Guangdong Province (2019B151502058), the Ma-jor Key Project of Peng Cheng Laboratory (PCL2021A09), and Industrial Key Technologies Program of Foshan (2020001006308,2020001006496)本文责任编委 许斌Recommended by Associate Editor XU Bin1. 华南理工大学自动化科学与工程学院 广州 5106412. 鹏城实验室 深圳 5180553. 山东大学控制科学与工程学院 济南2500611. School of Automation Science and Engineering, South China University of Technology, Guangzhou 5106412. Peng Cheng Laboratory, Shenzhen 5180553. School of Control Science and Engineering, Shandong University, Jinan 250061第 49 卷 第 9 期自 动 化 学 报Vol. 49, No. 92023 年 9 月ACTA AUTOMATICA SINICASeptember, 2023接、装配、医疗等领域, 相关技术也越来越受到科研人员重视[1−3]. 在机器人控制领域, 其控制目标之一就是实现机器人对特定任务轨迹的跟踪. 多自由度机器人作为一个高度耦合的非线性多输入多输出系统[4], 主要控制难点在于机器人工作环境任务多变,在外界扰动、负载变化、参数测量不精确等因素影响下, 机器人系统精确建模难度较大, 使得比例积分微分(Proportional-integral-derivative, PID)控制等经典控制算法难以满足机器人控制的精度要求. 针对机器人系统存在部分参数不确定或测量不准确的问题, 一些学者结合鲁棒控制、滑模控制等思想, 提出了许多有效的自适应控制算法[5−7]. 当机器人系统存在不可建模动态时, 一些学者结合神经网络的非线性函数逼近特性, 提出了大量的自适应神经网络控制方案, 保证了机器人在多变环境下的高性能控制[8−12].值得注意的是, 上述控制方案大多数都是基于力矩进行控制器设计, 其方案有效性主要是通过数值系统仿真进行验证, 鲜有在实际机器人上进行实验和应用. 造成上述现象的原因是, 当前市面上大部分工业/商业机器人并不开放力矩接口, 而是采用速度/位置控制. 这些封闭机器人采用标准的内外环控制结构, 其中外环为运动学环, 内环为动力学环, 内环控制的采样速率一般比外环要快得多,且普遍认为其内环控制使用速度比例积分(Propor-tional-integral, PI)控制器或位置PID控制器[13].封闭机器人的这些性质, 导致用户一般只能对其进行简单的运动学控制[14], 从而使得机器人难以应对多变的个性化产品加工. 针对这类具有内外环结构的机器人的控制问题, 部分学者提出了解决方案.文献[5, 15]在研究具有未知动力学和未知运动学的机械臂控制时, 提出了适当的自适应控制器. 文献[16]研究了一类具有关节速度反馈内环的机器人任务空间控制问题, 提出了一种基于模型的内环关节速度控制器通用结构[17]. 注意到, 上述控制方案中跟踪误差的收敛依赖于内环速度控制器的修改或再设计, 并不是常见的速度PI控制器或位置PID控制器, 对于具有不可修改内环的工业/商业机器人而言, 这些控制方案也难以实现应用. 进一步, 一些学者提出了预校正方案[18], 这些预校正方案的有效性验证主要是通过直观解释和实验结果进行的, 并没有进行严格理论分析. 针对封闭机器人控制存在的上述问题, 文献[19]在考虑机器人具有可以线性参数化的未知动力学和运动学且内环控制器参数未知和不可修改的背景下, 设计了一类外环自适应速度补偿控制器, 保证了机器人系统的稳定性和误差收敛. 该方案需要计算动力学和运动学回归矩阵, 这两个矩阵随着机械臂关节增多, 计算的复杂度呈指数倍增长. 此外, 实际机器人系统由于受到阻尼以及摩擦力等影响, 存在本质的非线性. 因此, 如何提出简单有效的封闭机器人控制方案, 既能实现封闭力矩的补偿控制, 又能精确建模未知非线性仍是一个开放性的问题.众所周知, 神经网络是建模未知非线性的有效方法[20]. 然而, 现有的大部分自适应神经网络控制并没有充分利用神经网络的学习能力, 即使是处理相同的任务也需要对神经网络进行重复训练, 该过程耗时长、计算资源消耗大、暂态阶段的控制性能也较差. 因此, 如何实现神经网络在控制过程中的学习和经验知识再利用是一个很有意义的课题[21].对此, 文献[22]提出确定学习理论, 解决了神经网络对未知动态的学习问题. 该理论证明了沿着回归轨迹的径向基函数(Radial basis function, RBF)神经网络满足持续激励(Persistent excitation, PE)条件, 进一步结合线性时变系统指数稳定性证明了神经网络权值的精确收敛. 基于该理论, 文献[23]引入动态面技术, 解决了自适应神经网络在严格反馈系统中的学习问题. 近年来, 确定学习理论也已被广泛应用于机器人编队控制[24]、心肌缺血早期诊断[25]、水面无人船控制[26]等领域, 在机械臂控制领域也有相关工作[27]. 然而, 现有基于确定学习的控制方案仍是基于力矩进行设计的, 无法在封闭的工业/商业机器人上直接进行应用.综上所述, 本文针对未开放力矩接口的一类封闭机器人系统, 在考虑机器人受到未知动力学影响且具有未知内环PI速度控制器的情况下, 基于文献[19]的外环补偿框架提出了一种基于外环速度补偿的确定学习控制方案, 实现了封闭机器人的关节轨迹跟踪控制. 该方案的主要贡献点如下: 1)在文献[19]的工作基础上, 引入神经网络处理系统未知动态, 取消了封闭机器人未知动力学模型参数线性化假设, 并简化了外环补偿控制设计过程; 2)采用宽度RBF神经网络动态增量神经网络节点, 降低了网络结构复杂度, 改善了系统控制的实时性;3)引入确定学习理论, 实现了宽度RBF神经网络对封闭机器人未知动态的精确学习, 并利用经验知识避免了对网络重复训练, 降低了计算负担, 实现了快稳准的高精度跟踪控制; 4)为确定学习理论应用于具有类似结构的封闭机械系统提供了研究思路, 拓展了确定学习的应用范围.1 问题描述及预备知识1.1 系统说明与控制目标n 本文所考虑的由永磁直流电动机驱动的自由9 期王敏等: 基于外环速度补偿的封闭机器人确定学习控制1905度机器人动力学模型[19]如下x 1∈R n M (x 1)∈R n ×nC (x 1,˙x1)∈R n ×n G (x 1)∈R n K ∈R n ×n u ∈R n 其中, 是机器人关节角位置; 是机器人的惯性矩阵; 是机器人的科氏力矩阵; 是机器人的重力向量; 是机器人内部的控制增益, 为一常值对角正定矩阵; 是封闭机器人的内环控制器.M (x 1)λm λM λm I ≤M (x 1)≤λM I I 性质 1. 机器人动力学方程的惯性矩阵 是对称并一致正定的, 且具有一致的界限, 存在正常数 和 使得 , 其中 为适当定义的单位矩阵.C (x 1,˙x 1)˙M (x 1)−2C (x 1,˙x 1)性质 2. 可以通过适当定义机器人动力学方程的科氏力矩阵 , 使得 是斜对称矩阵.在研究的封闭机器人内外环控制方法中, 本文考虑内环控制器为PI 速度控制器[19], 结构如下˙q c q c K p K i 其中, 和 是关节速度指令和关节位置指令, 是内环控制器的比例系数, 是内环控制器的积分系数, 均为未知对角正定矩阵.考虑如下光滑有界参考模型, 该模型将产生封闭机器人的关节期望轨迹x d 1∈R n x d 2∈R n f (x d 1,x d 2)y d =x d 1y d 其中, 和 分别是封闭机器人期望的关节角位置和角速度, 是给定的光滑非线性函数, 是封闭机器人期望输出. 本文假设期望输出 为周期轨迹.本文的控制目标是基于外环速度补偿控制思想, 在考虑封闭机器人具有不确定动力学和未知参数内环控制器的情况下设计系统(1)的速度控制指x 1y d 令, 从而确保: 1)机器人系统的所有信号都是最终一致有界的; 2)系统的输出 能够跟踪给定的期望输出轨迹 ; 3)在控制过程中学习机器人内部未知动态, 并利用学到的未知动态知识实现封闭机器人高精度跟踪控制. 控制方案框图如图1所示.1.2 RBF 神经网络1) RBF 神经网络的万能逼近特性: 为逼近机械臂控制过程中的未知非线性动态, 本文使用如下形式的RBF 神经网络f (Z )∈R n Z ∈ΩZ ΩZ W ∗∈R np p S (Z )=[s 1(Z −ξ1),···,s p (Z −ξp )]T s i (Z −ξi )=exp (−(Z −ξi )T (Z −ξi )/ηi )ξi =[ξi 1,···,ξin ]T ηi ϵ(Z )ΩZ f (Z )∥ϵ(Z )∥≤ϵ∗ϵ∗其中, , 是神经网络输入向量, 为一紧集, 是神经网络理想权值向量, 为RBF 神经网络隐含层节点数, 为回归向量, 此处选取高斯函数 作为径向基函数, 和 分别是神经元节点的中心和宽度, 是RBF 神经网络逼近误差. 文献[28]已经证明通过选取适当的神经元节点数、神经元中心和宽度, RBF 神经网络能够以任意精度逼近在紧集 上的任意光滑连续函数 , 即逼近误差 , 是一个任意小的正整数.ΩZ Z f (Z )2) RBF 神经网络的局部逼近能力: 基于文献[22], 对于紧集 内的任意有界轨迹 , 可以用沿着该轨迹的局部区域内有限数量的神经元逼近, 即S ζ(Z )=[s 1ζ(Z −ξ1ζ),···,s p ζ(Z −ξp ζ)]T S (Z )W ∗ζ∈R np ζW ∗p ζ<p ϵζ(Z )∥ϵζ(Z )∥−∥ϵ(Z )∥其中, 是回归向量 的子向量, 是神经网络理想权值向量 的子向量, 且 , 是局部RBF 神经网络逼近误差, 且 是一图 1 封闭机器人控制系统框图Fig. 1 Schematic diagram of manipulators with closed architecture control system1906自 动 化 学 报49 卷个极小值.S :[0,∞)→R s Λ1,Λ2,T 0定义 1[22]. 考虑一致有界且分段连续的向量函数 , 若存在大于零的常数 ,使得如下公式成立S I s ×s那么向量函数 满足PE 条件, 其中 定义为 维的单位矩阵.Z Z [0,∞)R q Z ΩZ ⊂R q ΩZ Z S ζ(Z )引理 1[22]. RBF 神经网络的局部PE 条件: 考虑任意回归/周期轨迹 , 假设 是从 到 的连续映射, 且 位于紧集 中. 则对于中心置于规则晶格(足够大到覆盖紧集 )上的RBF 神经网络, 只有中心位于回归/周期轨迹 的小邻域内的神经元才会被激励, 由其组成的回归子向量 将满足PE 条件.1.3 宽度RBF 神经网络在传统的RBF 神经网络逼近中, 需通过选取合适的神经元节点数、中心和宽度来保证逼近精度,而在实际应用中通常需要设计者根据自己的经验不断试错, 采用均匀布点的方式来设计RBF 神经网络的结构, 具有很强的主观性. 同时, 机器人控制系统是一个多输入多输出系统, 随着控制连杆数量的增加, RBF 神经网络的输入维数会呈几何倍数增长, 在均匀布点的设计方案下, 神经元数量也会急剧升高, 这将导致神经网络的计算负荷提高, 对硬件设备提出了更高的要求, 同时也将影响系统控制的实时性. 为了解决上述问题, 本文将使用文献[29]所提出的宽度RBF 神经网络方法进行网络结构设计. 该方法结合宽度神经网络增量节点的思想, 可实现在系统控制过程中神经元的自适应调整.宽度RBF 神经网络在初始化阶段以系统的初始状态为第一个神经元, 之后会根据神经网络的实际输入与网络已有神经元中心的距离来判断是否应该新增神经元. 新增神经元的增加策略如下:1)定义新增神经元所需参数ξn ,ηn ,W n 其中, 分别是新增神经元的中心、宽度和权值, 本文新增神经元的宽度设置与已有神经元一致, 权值统一初始化为零.2)判断当前网络输入是否超出现有神经元所构成的紧集域k C min ={c 1,···,ck }首先, 本文使用欧氏距离来描述当前网络输入与神经元中心点的距离, 根据距离选取离当前输入最近的 个点集 , 则可由下式获得新增神经元的中心βC min 0∼1¯Cmin C min ¯Cmin =(c 1+c 2+···+c k )/k 其中, 是决定新增神经元与神经元集合 之间距离的可调参数, 取值范围为 ; 是神经元集合 的平均中心位置, .εZ C min ¯Cmin ε然后, 设置判断是否新增神经元的可调阈值 ,当神经网络当前输入 与神经元集合 的平均中心位置 之间距离大于阈值 时, 添加新的神经元, 否则保持原有神经元集合不变.2 外环自适应神经网络速度控制指令设计本节将针对系统(1), 采用反步法进行基于外环补偿的速度控制指令设计. 首先将封闭机器人系统的内环速度PI 控制器(2)代入系统(1), 将系统(1)转化为如下形式K P =KK p K I =KK i y y =x 1其中, , , 是机器人系统输出关节角位置, .根据传统反步法设计思想, 定义如下误差变量α1α1˙q c 其中, 是虚拟控制器. 考虑系统(8), 接下来的反步设计包括两个步骤, 将依次设计出虚拟控制器 和速度控制指令 . 具体设计过程如下:z 1步骤 1. 考虑系统(8)以及误差定义(9), 对 求导得α1根据式(10), 虚拟控制器 可设计为c 1其中, 为控制增益, 且为正的设计参数.z 2步骤 2. 根据误差定义(9), 对 求导可得考虑封闭机器人系统具有未知的动力学和内环速度PI 控制器, 定义未知系统动态为Z 1=[x T 1,x T 2,q T c ,˙αT 1]T ∈R 4n n f (Z 1)其中, , 是机器人自由度. 使用RBF 神经网络来逼近未知动态 , 得9 期王敏等: 基于外环速度补偿的封闭机器人确定学习控制1907ϵ1(Z 1)∥ϵ1(Z 1)∥≤ϵ∗ϵ∗W ∗1∈R nϖϖW ∗1ˆW 1˜W 1=ˆW 1−W ∗1其中, 是逼近误差, 且 , 是任意小的正数, 是未知的理想权值向量, 为宽度RBF 神经网络节点个数. 令 的估计值为, 则权值估计误差为 .将式(14)代入式(12)可得ˆW1W ∗根据式(15), 利用权值估计值 代替理想权值 , 设计自适应神经网络速度控制指令如下并构造神经网络权值估计值更新率为c 2˙q c γσσ其中, 是速度控制指令 的控制增益, 且为正的可设计参数; , 分别是神经网络权值估计值更新率的控制增益和 修正项, 均为正的待设计参数.q c ˙qc q c 注 1. 在考虑未知动力学影响的机器人自适应神经网络控制器设计中, 现有大部分成果均为力矩控制器, 无法应用于本文所考虑的封闭机器人系统.本文在机器人具有未知不可修改内环速度PI 控制器的背景下, 设计了与内环相匹配的外环自适应神经网络速度控制指令. 该指令与常见力矩控制器的代数方程形式不同, 是一个关于 的一阶微分方程,通过求解该微分方程, 可以获得输入机器人系统的速度控制指令 和位置控制指令 , 同时, RBF 神经网络的应用使该速度控制指令具有适应机器人未知动力学影响和未知内环控制器的能力.q c K,K p ,K i K −1P K I q c 注 2. 与现有基于反步法的自适应神经网络力矩控制器相比, 本文所设计速度控制指令在神经网络输入上将多出一个信号 . 这是因为在控制器设计过程中, 为了处理内环速度PI 控制器的未知参数 带来的不确定性, 本文在定义未知系统动态的时候将 考虑在内, 从而有助于后续的控制器设计以及未知动态的精确神经网络逼近.至此, 可得封闭机器人的闭环系统动态如下µ>0V (0)≤µc 1c 2γσz 1z 2定理 1. 考虑由封闭机器人系统(8)、参考模型(3)、自适应神经网络速度控制指令(16)和神经网络权值估计值更新率(17)所组成的闭环系统, 那么对于任意给定的常数 以及所有满足 的系统初始状态,则通过选取合适的设计参数 ,, 和 , 可以使得闭环系统中的所有信号是最终一致有界的, 并且跟踪误差 , 能够收敛到零的小邻域内.证明. 选取如下Lyapunov 函数结合机器人动力学方程性质2, 沿系统(18)所产生的轨迹对所选Lyapunov 函数求导可得利用Young 不等式对Lyapunov 函数的导数放缩得λmin (K P )K P 其中, 是矩阵 的最小特征值.结合式(19)和式(21)可得其中a >b /µV =µ˙V≤0V ≤µV (0)≤µt >0V (t )≤µ至此, 只要选择 , 那么可以保证当 时, , 因此 是一个不变集, 即对于任意满足 的初始条件, 对于任意 , 有 .进一步, 对式(22)积分可得其中c 1,c 2,从上式可知, 通过选取合适的设计参数 1908自 动 化 学 报49 卷γ,σθ, 可使得 任意小. 因此, 闭环系统中的所有信号是最终一致有界的.进一步, 从式(19)及式(23)可得ν1>√2θ/λmin (K P )ν2>√2θ/λm T 1t>T 1令 , , 则存在一个有限时间 , 对于任意 有θν1,ν2z 1,z 2T 1从上述分析可知, 选取合适的设计参数可使 任意小, 即 可以任意小, 因此跟踪误差 可以在有限时间 内收敛到零的小邻域内.□3 基于确定学习的速度补偿控制T 1在第 2 节, 本文针对封闭机器人系统(8)设计了外环自适应神经网络速度控制指令(16)以及神经网络权值估计值更新率(17), 并证明了系统在该控制指令的作用下是最终一致有界的, 且系统跟踪误差可在有限时间 内收敛于零的小邻域内. 本节将基于确定学习理论[22], 进一步验证神经网络对封闭机器人系统(8)未知动态的准确学习, 且实现学习后的常值神经网络权值的表达与存储.y d ˆW 1(0)=0W ∗1Z 1diag {S T 1(Z 1),···,S T 1(Z 1)}¯W1定理 2. 考虑由封闭机器人系统(8)、参考模型(3)、自适应神经网络速度控制指令(16)和神经网络权值估计值更新率(17)所组成的闭环系统, 对于任意给定的回归期望轨迹 , 有界的初始条件以及, 神经网络权值估计值将收敛到理想权值 的小邻域内, 且沿着回归输入信号 的常值RBF神经网络 , 即ϵ1(Z 1)∥ϵ1(Z 1)∥≤ϵ∗ϵ∗¯W1其中, 是神经网络对未知系统动态的逼近误差, 且 , 是一个任意小的正整数, 且常值神经网络权值 的表达式为t b >t a >T 1[t a ,t b ]其中, , 是系统达到稳态后的一段时间.证明. 证明分为以下两个部分进行:Z 11)神经网络输入 回归性证明.y d ,˙y d ,¨y d T 1z 1=x 1−y d y =x 1y d α1=−c 1z 1+˙y d 根据给定光滑有界参考模型(3), 期望轨迹 均为周期轨迹. 由定理1可知, 闭环系统内的所有信号均有界且跟踪误差在有限时间 内收敛到零的小邻域. 根据 , 封闭机器人的输出关节角位置 能够跟踪上给定的期望周期轨迹 ; 虚拟控制器 将跟踪上周˙y d z 2=x 2−α1x 2˙y d ˙α1=−c 1(x 2−˙y d )+¨y d ¨y d 期轨迹 . 根据跟踪误差 , 能够跟踪上周期轨迹 , 故虚拟控制器的导数 将跟踪上周期轨迹 .x 2α1˙z 2=˙x 2−˙α1M (x 1)x 1M (x 1)z 1,z 2,ϵ1(Z 1)diag {S T 1(Z 1),···,S T 1(Z 1)}˜W1˜W1S 1(Z 1)˙qc =−diag {S T1(Z 1),···,S T 1(Z 1)}ˆW 1−c 2z 2−z 1˜W 1ˆW 1˙q c 因为 和 均为周期信号, 故 为周期信号. 考虑封闭机器人系统的惯性矩阵 ,当机器人所有的运动关节为转动关节时, 矩阵中仅含有 的正弦函数和余弦函数元素, 因此惯性矩阵 是周期信号. 又 均为任意小的值, 从式(18)可得, 是周期信号且有界, 根据定理1有 有界, 且对于所有的神经网络输入 有界. 则考虑外环自适应神经网络速度控制指令 , 由 有界可得 有界, 故 是有界信号.x 1,˙x 1C (x 1,˙x 1)G (x 1)˙q c +K −1P K I q c q c 根据科氏力矩阵和重力矩阵的定义可知, 当 为周期信号时, 和 亦是周期信号. 结合式(12)可得, 为周期信号且有界, 则 有界且满足回归性.t T 1Z 1=[x T 1,x T 2,q T c ,˙αT1]S 1(Z 1)综上所述, 当时间 超过有限时间 后, 神经网络输入 是满足回归性的. 进一步借由引理1, 可以得到回归向量 满足局部PE 条件.2)构建线性时变系统及其稳定性证明.Z 1使用沿着回归信号 的局部RBF 神经网络对未知系统动态进行逼近, 并考虑由(15)、(16)和(17)组成的闭环子系统有S 1ζ(Z 1)S 1(Z 1)Z 1ˆW 1ζ¯1ζZ 1∥diag {S T¯1ζ(Z 1),···,S T ¯1ζ(Z 1)}ˆW ¯1ζ∥Z 1ϵ1ζ(Z 1)=ϵ1(Z 1)−diag {S T ¯1ζ(Z 1),···,S T ¯1ζ(Z 1)}˜W ¯1ζ∥ϵ1ζ(Z 1)∥∥ϵ1(Z 1)∥其中, 是回归向量 的子向量, 是由回归轨迹 附近被激活的神经元构成的; 是权值估计值向量的子向量. 式(28)中下标 表示远离回归轨迹 的神经元, 这部分神经元不会被激活, 其权值将会在零附近, 因此 将是一个较小的值. 沿着回归轨迹 的局部RBF 神经网络逼近误差为 , 且 将接近于 .M (x 1)K P 考虑到 的存在可能使得神经网络的逼近误差项被放大, 这将导致即使闭环系统(27)标9 期王敏等: 基于外环速度补偿的封闭机器人确定学习控制1909e 2=K −1PM (x 1)z 2称部分的指数稳定性得到证明, RBF 神经网络的学习能力也无法实现. 对此, 本文引进一个新的误差变量 来避免上述问题, 借由新误差变量可将系统(27)转化为如下带小摄动项的线性时变系统形式H (Z 1)=diag {S 1ζ(Z 1),···,S 1ζ(Z 1)}ϵ′1ζ=−z 1+ϵ1ζA =−(c 2+K −1P C (x 1,˙x 1)−K −1P˙M (x 1))×M −1(x 1)K P ,P =γM −1(x 1)K P 其中, , , .ϵ′1ζσσγˆW1ζc 2˙P+P A +A T P <0ϵ′1ζ−σγˆW 1ζe 2˜W1ζT 1根据定理1可知, 是一个极小值, 通过选取较小的参数 也可使 是一个极小值, 故系统(29)是一个带有小摄动项的线性时变系统. 随后通过选取合适的参数 , 可使得 . 进一步运用文献[23]的方法可证明系统(29)中标称部分的指数稳定性. 此外, 由于通过选取合适参数可使得摄动项 和 非常小, 因此借由文献[30]中的小摄动定理(引理4.6), 可保证误差 和 在有限时间 内均可收敛到零的小邻域内.ˆW1W ∗根据上述证明, 权值估计值 最终会收敛于理想权值 , 故可运用式(26)所存储的常值权值构造常值RBF 神经网络¯ϵ1ζ¯ϵ1ϵ1ζϵ1其中, 和 分别接近 和 .□进一步, 运用所获常值RBF 神经网络, 可获基于确定学习的速度控制指令如下ρ>0U (0)≤ρc 1c 2z 1定理 3. 考虑由封闭机器人系统(8)、参考模型(3)、基于确定学习的速度控制指令(31)所组成的闭环系统, 对于任意给定的常数 以及所有满足 的系统初始状态, 则通过选取合适的待设计参数 , 可使得闭环系统中的所有信号是最终一致有界的, 并且跟踪误差 能够收敛到零的小邻域内.该证明与定理1的证明过程类似, 此处略.注 3. 基于自适应神经网络的速度补偿控制方案需要在线自适应调整神经网络估计权值, 主要适用于控制任务变化的工作场景. 基于确定学习的速度补偿控制方案包括两个工作阶段: 神经网络训练和经验利用. 神经网络训练阶段, 即自适应调节过程, 该阶段适用任务多变的工作场景; 经验利用阶段, 即利用训练阶段获取的未知动态知识构造神经网络学习控制器, 提升系统的暂态控制性能和降低在线计算量, 主要适用于与训练阶段控制任务相同或相似的工作场景.4 实验验证为验证本文所提方案的有效性, 本节将分别在双连杆封闭机器人数值系统和实际UR5机器人平台上进行实验验证. UR5机器人作为市面上常见的工业机器人, 其力矩控制接口不予开放, 一般只可做运动控制, 符合本文封闭机器人的研究背景.4.1 数值仿真本节将对定理1所提自适应控制方案以及定理3所提学习控制方案进行对比实验, 以验证RBF神经网络在稳定自适应控制过程中的学习和知识再利用能力, 并分别使用均匀布点和宽度RBF 神经网络两种网络构造方式完成上述对比实验, 以验证宽度RBF 神经网络的优越性. 考虑由(1)和(2)组成的双连杆封闭机器人动力学模型x 1=[x 1,1,x 1,2]T x 1,1x 1,2其中,, 和 分别代表封闭机器人的关节1角位置和关节2角位置, 且各矩阵为a 1=m 2l 22+(m 1+m 2)l 21a 2=2l 1l 2m 2a 3=m 2l 22a 4=(m 1+m 2)l 1g a 5=m 2l 2g m 1,m 2l 1,l 2g 其中, , , , , , 分别是连杆1和连杆2的质量, 分别是连杆1和连杆2的长度, 是重力加速度.m 1=0.8m 2=2.3l 1=1l 2=1g =9.8y d =[y d 1,y d 2]T =[0.5sin (0.5t )+0.3sin (t ),0.3sin (0.5t )+0.5sin (t )]T K =diag {10,10}K p =diag {2,2}K i =diag {15,15}x 1=[0,实验所用系统参数设置为: kg, kg, m, m, m/s 2, 系统所选期望轨迹为 , 系统内环控制器增益 , 比例系数 ,积分系数 . 系统初始状态为 1910自 动 化 学 报49 卷。
Learning-Based Control Robot Training
Learning-Based Control Robot Training Learning-based control robot training is an essential aspect of developing robots that can effectively navigate and interact with their environment. This type of training involves using machine learning algorithms to teach robots how to perform various tasks, such as grasping objects, navigating through a space, or interacting with humans. However, there are several challenges and considerations that need to be addressed when it comes to training robots using learning-based control.One of the main challenges of learning-based control robot training is the need for large amounts of high-quality training data. Machine learning algorithms require a significant amount of data to effectively learn and generalize from. This means that in order to train a robot to perform a task, such as grasping objects, researchers need to collect and label a large dataset of examples of the robot successfully completing the task. This can be a time-consuming and labor-intensive process, as it requires manually collecting and annotating the data, which can be challenging and expensive.Another challenge of learning-based control robot training is the need for robust and generalizable algorithms. Machine learning algorithms are often trained on specific datasets, which can lead to overfitting and poor generalization to new environments or tasks. This means that a robot trained using learning-based control may struggle to adapt to new situations or tasks that were not included in its training data. Developing algorithms that are robust and generalizable is a critical consideration in training robots using learning-based control, as it ensures that the robot can effectively operate in a wide range of environments and scenarios.In addition to these technical challenges, there are also ethical considerations that need to be taken into account when training robots using learning-based control. For example, as robots become more advanced and capable of performing complex tasks, there is a concern about the potential impact on the job market and the displacement of human workers. It is essential to consider the ethical implications of training robots to perform tasks that were previously done by humans and to ensure that the development anddeployment of these robots are done in a way that is fair and equitable for all members of society.Furthermore, there is also a need to consider the safety and reliability of robots trained using learning-based control. As robots become more autonomous and capable of performing a wide range of tasks, there is a concern about the potential for accidents or errors that could cause harm to humans or property. It is crucial to develop robust and reliable training methods that prioritize safety and ensure that robots are capable of operating in a way that minimizes the risk of accidents or errors.Despite these challenges and considerations, learning-based control robot training offers significant potential for developing robots that can effectively navigate and interact with their environment. By leveraging machine learning algorithms, researchers can teach robots to perform complex tasks and adapt to new situations, ultimately leading to more capable and versatile robots. However, it is essential to address the technical, ethical, and safety considerations associated with training robots using learning-based control to ensure that the development and deployment of these robots is done in a responsible and ethical manner.。
inquiry based learning教学法
inquiry based learning教学法Inquiry-based Learning (IBL) is a teaching method that centers around encouraging students to actively participate in their own learning process, with the goal of developing critical thinking skills, problem-solving abilities, and a deep understanding of the subject matter. Instead of simply memorizing facts or following instructions, inquiry-based learning emphasizes students' exploration, questioning, and investigation.One of the key principles of IBL is the shift from a teacher-centered to a student-centered approach. In aninquiry-based classroom, the teacher acts as a facilitator rather than a lecturer. The teacher's role is to guide and support students as they navigate through a series of open-ended questions and challenges. This approach promotes a more engaging and interactive learning experience.Inquiry-based learning promotes the development of various skills, including research skills, communication skills, and critical thinking skills. When encountering a new topic or problem, students are encouraged to generate their own questions and design investigations to find answers. Through this process, they learn how to gather and analyze information, form logical arguments, and communicate their findings effectively.Furthermore, inquiry-based learning enhances students' motivation and engagement in learning. By allowing students to explore topics of interest and pursue their own inquiries, they become more invested in their own learning journey. This method encourages curiosity, creativity, and independentthinking, fostering a deep sense of ownership and pride intheir accomplishments.Implementing IBL in the classroom requires carefulplanning and design. Teachers need to create a supportive and stimulating environment that encourages risk-taking and collaboration. They should provide resources and materialsthat facilitate exploration and investigation. Additionally, the teacher should establish clear learning goals, objectives, and evaluation criteria to ensure that students stay on track and meet the required academic standards.In conclusion, inquiry-based learning is a powerful teaching method that empowers students to take control oftheir own learning. By fostering curiosity, critical thinking, and problem-solving skills, it equips students with the necessary tools to thrive in an ever-changing world. Through active engagement and exploration, students become active learners, enabling them to develop a deep understanding ofthe subject matter and apply their knowledge in real-world contexts.。
(项目管理)项目专业术语
项目范围管理基准计划（baseline）概念开发（conceptual development）配置管理（configuration management）控制系统（control system）成本控制账户（cost control accounts）成本加成合同（cost-plus contracts）可交付成果（deliverable）里程碑（milestone）组织分解结构（organization breakdown structure, OBS）项目收尾（project closeout）项目范围（project scope）责任分配矩阵（responsibility assignment matrix, RAM）范围基准计划（scope baseline）范围蔓延（scope creep）范围管理（scope management）范围报告（scope reporting）范围说明（scope statement）工作说明书（statement of work, SOW）总承包合同（turnkey contracts）工作分解结构代号（WBS codes）工作授权（work authorization）工作分解结构（work breakdown structure, WBS）工作包（work package）项目风险管理可能性和后果分析(analysis of probability and consequences)变更管理(change management)商业风险(commercial risk)应急储备金(contingency reserves)合约/法律风险(contractual/legal risk)控制和文档化(control and documentation)交叉培训(cross-training)执行风险(execution risk)财务风险(financial risk)固定总价合同(fixed-price contract)违约赔偿金(liquidated damage)管理应急金(managerial contingency)指导(mentoring)项目风险(project risk)项目风险分析和管理(project risk analysis and management, PRAM) 风险识别(risk identification)风险管理(risk management)风险缓解策略(risk mitigation strategies)任务应急金(task contingency)技术风险(technical risk)项目团队的建设、冲突和谈判可接近性（accessibility）中止（adjourning）管理上的冲突(administrative conflict)凝聚力(cohesiveness)冲突(conflict)跨职能合作(cross-functional cooperation) 差异化(differentiation)成立阶段(forming stage)挫败(frustration)基于目标的冲突(goal-oriented conflict)互动(interaction)相互依赖(interdependencies)个人之间的冲突(interpersonal conflict)谈判(negotiation)规范化阶段(norming stage)目标(orientation)结果(outcome)实施阶段(performing stage)物理位置上的接近(physical proximity)原则性谈判(principled negotiation)社会心理结果(psychosocial outcomes)中断平衡(punctuated equilibrium)冲突风暴(storming)最高目标(superordinate goals)任务结果(task outcomes)团队建设(team building)信任(trust)虚拟团队(virtual teams)成本估算和预算基于活动的估算（ABC, activity-based costing）自下而上的预算(bottom-up budgeting)应急费用预算(budget contingency)成本估算(cost estimation)赶工(crashing)最终估算(definitive estimates)直接成本(direct costs)加速成本(expedited costs)可行性估算(feasibility estimates)固定成本(fixed costs)间接成本(indirect costs)学习曲线(learning curve)一次性成本(nonrecurring costs)正常成本(normal costs)参数估算(parametric estimation)项目预算(project budget)经常性成本(recurring costs)分阶段预算(time-phased budget)自上而下的预算(top-down budgeting)变动成本(variable costs)项目进度计划：网络、历时估计和关键路径活动，也称任务(activity, or task)双代号网络图法(activity-on-arrow)单代号网络图法(activity-on-node)箭线(Arrow)逆推法(backward pass)β分布(beta distribution)发散活动(burst activity)并行活动(concurrent activities)置信区间(confidence interval)赶工(crashing)关键路径(critical path)关键路径法critical path method ()历时估计(duration estimation)最早开始时间(early start date, ES)事件(event)浮动时差(float, or slack)正推法(forward pass)集合活动(hammock activities)阶梯化活动(laddering activities)最晚开始时间(late start date)汇聚活动(merge activities)网络图(network diagram)节点(node)排好序的活动(ordered activities)路径(path)前置活动(predecessors)计划评审技术(program evaluation and review technique) 项目网络图(project network diagram, PND)项目计划编制(project planning)有限资源进度计划(resource-limited schedule)范围(scope)串行活动(serial activities)后续活动(successors)任务(task)工作分解结构(work breakdown structure)工作包(work package)项目进度计划：滞后、赶工和活动网络活动（也称任务）（activity,也称task）双代号网络图（activity-on-arrow, AOA）单代号网络图（activity-on-node, AON）箭线(Arrow)逆推法(backward pass)赶工(crashing)关键路径（critical path）虚活动（dummy activities）最早开始时间(early start date, ES)事件（event）浮动时差(float, or slack)正推法(forward pass)甘特图（Gantt chart）滞后（lag）最晚开始时间(late start date)汇聚（merge）节点（node）计划评审技术（Program Evaluation and Review Technique, PERT）串行活动（serial activities）后续活动（successors）任务（task,见activity）关键链项目进度计划能力约束缓冲（capacity constraint buffer, CCB）中心极限理论（central limits theorem）普通原因偏差（common cause variation）关键链（critical chain）关键链项目管理（critical chain project management, CCPM）鼓点（drum）鼓点缓冲（drum buffers）多任务处理（multitasking）消极偏差（negative variation）积极偏差（positive variation）特殊原因偏差（special cause variation）学生综合症（student syndrome）约束理论（theory of constraints, TOC）资源管理平衡试探法（leveling heuristics）混合约束型项目（mixed-constraint project）物质约束（physical constraints）资源约束型项目（resource- constrained project）资源约束（resource constraints）资源平衡（resource leveling）资源负载（resource loading）资源负载图（resource-loading charts）资源负载表（resource-loading table）平滑（smoothing）分割活动（splitting activities）时间约束型项目（time-constrained project）项目评估和控制已完成工作实际成本（actual cost of work performed, AC）完工预算（budgeted cost at completion, BAC）控制循环（control cycle）成本绩效指数（cost performance index, CPI）挣值（earned value, EV）挣值管理（earned value management, EVM）里程碑（milestone）计划值（planned value, PV）项目基准计划（project baseline）项目控制（project control）项目S曲线（project S-curve）进度绩效指数（schedule performance index, SPI）进度偏差（schedule variance）跟踪甘特图（tracking Gantt charts）项目收尾和中止仲裁（arbitration）建造－经营－转让（build, operate, transfer, BOT）建造－拥有－经营－转让（build, own, operate, transfer, BOOT）默认索赔（default claims）争议（disputes）提前终止（early termination）特惠索赔（ex-gratia claims）经验教训（lessons learned）自然终止（natural termination）私人主动融资（private finance initiatives, PFIs）项目终止（project termination）附加式终止（termination by addition）绝对式终止（termination by extinction）集成式终止（termination by integration）自灭式终止（termination by starvation）非自然终止（unnatural termination）。
BTEC Level 6 迷你舱飞行器系统工程教程（QCF）单元 T22：飞行器系统工程说明书
NIT VIONIC YSTEMS NGINEERING Unit T22: Avionic SystemsEngineeringUnit code: R/504/0134QCF level: 6Credit value: 15AimThe aim of this unit is to provide learners with a detailed knowledge and understanding of a wide range of avionic and radar systems used in aircraftand how they are integrated into more complex flight management systemsto provide efficient, reliable and safe control of an aircraft.Unit abstractEffective avionic systems are crucial to the operation of all modern aircraft.This unit will develop learner understanding of a wide range of avionic systems. Learners will begin by investigating and analysing representative aircraftsystems used for communication, navigation, transponder, collision avoidance, flight monitoring and automatic control. They will also test and evaluate the different types of antenna used for avionic applications. Learners will continueby investigating the principles, operation and application of aircraft radar and ground radar systems. They will critically evaluate these systems and analysethe performance of representative radar antennas.Aircraft data bus systems allow a wide variety of avionics equipment to communicate with one another and exchange data. Learners will investigate representative data bus systems demonstrating their use in a modern aircraft where a number of complex avionic systems need to be interoperable and yet remain tolerant of faults which can potentially propagate through the system. Finally, learners will analyse, evaluate and demonstrate systems used to ensure the safe, reliable and cost-effective operation of an aircraft. These include systems used for gathering, processing and displaying information about an aircraft’s position, altitude and motion, as well as automatically controlling an aircraft in flight and during landing.NIT VIONIC YSTEMS NGINEERINGLearning outcomesOn successful completion of this unit a learner will:1 be able to analyse the avionic systems used in modern aircraft2 understand the principles, operation and application of aircraft and ground radar systems3 understand aircraft data bus and integrated avionic systems4 understand systems for flight monitoring, automatic flight control (AFCS) and flight management (FMS).NIT VIONIC YSTEMS NGINEERING Unit content1Be able to analyse the avionic systems used in modern aircraft Communication systems: HF voice; VHF voice; satellite voice; HF data link(HFDL); VHF data link (VHFDL); aircraft communications addressing andreporting system (ACARS); amplitude modulation (AM); frequency modulation (FM) single-sideband (SSB); multiple frequency shift keying (MFSK); phase shift keying (PSK)Navigation systems: VHF omnidirectional radio range (VOR); tactical airnavigation (TACAN); automatic direction finder (ADF); distance measuringequipment (DME); global positioning system (GPS) and global navigationsatellite system (GNSS); inertial navigation systems (INS); Kalman filters;errors and correctionsSurveillance systems: traffic alert and collision avoidance system (TCAS); air traffic control transponder systems (Modes A, C and S); automatic dependent surveillance-broadcast (ADS-B)Antennas: Ultra High Frequency (UHF); Very High Frequency (VHF); HighFrequency (HF); omnidirectional; directional; steerable; satellitecommunications (Satcom); low profile; antenna feeders; coupling units;switching units; matching units2Understand the principles, operation and application of aircraft and ground radar systemsAircraft radar: pulsed radar systems; Continuous-wave (CW) radar systems;primary, secondary and Doppler radar systemsAntennas: horn; flat plate array; reflector typesGround radar: ground movement; surveillance; precision approach radar;air traffic control radarPerformance parameters: mean and peak power; range; bearing; height;radar equations.3Understand aircraft data bus and integrated avionic systems Aircraft data bus systems: bus architecture; bus protocols; bus timing; buscontrol; data word format; physical bus connections; bus termination; ARINC 429; ARINC 573; ARINC 629; MIL-STD-1553B/1773B; CSDB; ASCB; FDDIIntegrated avionic systems: flight data recorder (FDR); electronic aircraftmonitoring systems (ECAM); built-in test equipment (BITE).NIT VIONIC YSTEMS NGINEERING4Understand systems for flight monitoring, automatic flight control (AFCS) and flight management (FMS)Flight monitoring systems: gyroscopic systems; magnetic systems; air data systems; air data computers; electronic displays; data recording; electronic attitude director indicator (EADI); electronic horizontal situation indicator(EHSI); electronic attitude director indicator (EADI); multi-function displays Automatic flight control systems (AFCS): integrated flight control systems;flight directors; auto throttle; auto pilot; instrument landing systems (ILS);auto land systemsFlight management systems: aircraft navigation database; required navigation performance (RNP); lateral navigation (LNAV); vertical navigation (VNAV);factors affecting fuel efficiency.NIT VIONIC YSTEMS NGINEERING Learning outcomes and assessment criteriaLearning outcomesOn successful completion of this unit a learner will: Assessment criteria for pass The learner can:LO1 Be able to analyse the avionic systems used in modern aircraft 1.1 Critically evaluate High Frequency (HF), VeryHigh Frequency (VHF) and satellite-basedcommunication systems1.2 Critically evaluate aircraft navigation andposition fixing systems1.3 Critically evaluate collision avoidance and airtraffic control transponder systems1.4 Critically evaluate the performance of antennassuitable for use on aircraft, space vehicles and ground stationsLO2 Understand the principles, operation and application, of aircraft radar and ground radar systems 2.1 Identify the key factors necessary for thesatisfactory operation of aircraft and groundradar systems2.2 Critically evaluate weather radar, ground radarand aircraft tracking systems2.3 Critically evaluate the performance of horn,flat plate array and reflector radar antennasLO3 Understand aircraft data bus and integrated avionic systems 3.1 Develop a rationale for the integration andinteroperation of aircraft avionic systems3.2 Critically evaluate current aircraft data bussystems that comply with ARINC standards,using investigation results3.3 Critically evaluate the performance of integratedavionic systems in relation to fault tolerance,using investigation resultsLO4 Understand systems for flight monitoring, automatic flight control (AFCS) and flight management (FMS) 4.1 Critically evaluate the performance of flightmonitoring systems including sensors andassociated data acquisition, processing anddisplay systems, using investigation results 4.2 Critically evaluate automatic flight controlsystems (AFCS) including autopilot andautomatic landing systems, using investigation results4.3 Develop a rationale for an integrated flightmanagement system (FMS).NIT VIONIC YSTEMS NGINEERING GuidanceLinks to National Occupational Standards, other BTEC units, other BTEC qualifications and other relevant units and qualificationsThe learning outcomes associated with this unit are closely linked with:Level 4 Level 5 Level 6Unit 85: Automatic Flight Control Systems Unit 35: Further Analytical Methods for EngineersUnit T6: DataCommunication andSensor Networks Unit 59: Advanced Mathematics for Engineering Unit T7: Modelling andSimulation for EngineersUnit 82: Aircraft Systems Principles and Applications Unit T8: Digital Signal ProcessingUnit 91: Integrated Flight Instrument Systems Unit T10: Embedded Systems in Engineering Unit T12: DigitalCommunications inEngineeringThe content of this unit has been designed and mapped against the Engineering Council’s current learning outcomes for IEng accreditation. The completion of the learning outcomes for this unit will contribute knowledge, understanding and skills towards the evidence requirements for IEng registration.See Annexe B for a mapping of the Edexcel BTEC Level 6 Diploma units toIEng programmes.Essential requirementsRepresentative examples of:● aircraft communication, navigation, surveillance and radar systems, includingdisplays, controllers and antennas. This should include an allocation of timeon representative aircraft and/or simulators.● avionic test equipment including Radio Frequency (RF) test gear (powermeter, modulation meter, digital frequency meter, Standing Wave Radio(SWR) bridge, etc.)● bus analyser compatible with the bus systems in use (eg ARINC 429,ARINC 573, ARINC 629, ARINC 708, MIL-STD-1553B/1773B, FDDI)● antenna test equipment (RF anechoic chamber or outside test range).Learners will also need access to appropriate software (eg Matlab or Scilab)for simulation purposes● ARINC documentation and standards (see ‘Other publications’).NIT VIONIC YSTEMS NGINEERINGDeliveryThe emphasis for this unit should be on avionic systems rather than theindividual components that make up those systems. The unit should ideally involve mixed delivery methods based on the use of whole-class lectures, practical investigations, work-based learning assignments, tutorials andone-to-one consultation with subject specialists.It is desirable that this unit is taught using case studies that put the avionic systems into context and also motivate learners. The assignment(s) shouldthen be based on critical evaluation of these systems, requiring learners to demonstrate their understanding of the principles, operation, applications and limitations of the systems. This will require access to working systems and relevant documentation (see ‘Other publications’).Learners will require access to typical avionic systems either on ‘live’ aircraftand/or simulators or by making use of working systems within a laboratoryor workshop environment. Learners should be encouraged to explore new innovations in avionics rather than those that rely on outdated technology.A total of 150 hours of notional learning time is recommended for this unit comprising the following:1) A lecture program of 2 hours per week (1.5 hours lecture and 0.5 hoursdiscussion/seminar/tutorial) for a total of 20 weeks is suggested as formallearning time (40 hours).2) Workshop/laboratory-based sessions of 1 hour per week for 20 weeks wherelearners work in groups carrying out structured investigations and testing of avionic systems with tutor or technician support.3) Self-study time for learners to work in groups to carry out their investigations,undertake additional research and prepare and present their findings(90 hours).It is strongly recommended that learners work in small groups (of not morethan four learners) and receive individual marks for their work. This willrequire: (i) supervisors sitting in on some formal meetings, (ii) each page inthe report produced by the group must show the name of the learner primarily responsible for the contents and (iii) a system of peer review.AssessmentTwo or three group reports and presentations as described previously in the Delivery section.NIT VIONIC YSTEMS NGINEERINGResourcesTextbooksCollinson R P G – Introduction to Avionics (Springer, 2011)ISBN 978-9400707078Federal Aviation Administration – Advanced Avionics Handbook(FAA Handbooks, 2009) ISBN 978-1560277583Helfrick A – Principles of Avionics (Airline Avionics, 2007)ISBN 978-1885544261Macnamara T M – Introduction to Antenna Placement and Installation(John Wiley and Sons 2010) ISBN 978-0470019818Moir I, Seabridge A and Jukes M – Civil Avionics Systems (Wiley, Blackwell, 2006) ISBN 978-0470029299Spitzer C – Avionics: Development and Implementation (Avionics Handbook) (CRC Press, 2006) ISBN 978-0849384417Spitzer C – Avionics: Elements, Software and Functions (Avionics Handbook) (CRC Press, 2006) ISBN 978-0849384387Tooley M – Aircraft Communication and Navigation Systems(Butterworth-Heinemann, 2007) ISBN 978-0750681377Tooley M – Aircraft Digital Electronic and Computer Systems(Butterworth-Heinemann, 2007) ISBN 978-0750681384Tooley M and Wyatt D – Aircraft Electrical and Electronic Systems (Butterworth-Heinemann, 2009) ISBN 978-0750686952Other publicationsThe following is a selection of publications available from ARINC (Aeronautical Radio, Incorporated) at :●ARINC Specification 429P1-17 Mark 33 Digital Information Transfer System (DITS), Part 1, Functional Description, Electrical Interface, Label Assignments and Word Formats●ARINC Specification 429P2-16 Mark 33 Digital Information Transfer System (DITS), Part 2 - Discrete Data Standards●ARINC Specification 429P3-19 Mark 33 Digital Information Transfer System (DITS) - Part 3 - File Data Transfer Techniques●ARINC Specification 429P3-19 Mark 33 Digital Information Transfer System (DITS) - Part 3 - File Data Transfer Techniques●ARINC Characteristic 578-4 Airborne ILS Receiver●ARINC Characteristic 579-2 Airborne VOR Receiver●ARINC Characteristic 591 Quick Access Recorder for AIDS System (QAR)NIT VIONIC YSTEMS NGINEERING ●ARINC Characteristic 594-4 Ground Proximity Warning System●ARINC Report 600-19 Air Transport Avionics Equipment Interfaces●ARINC Report 629 Part 1-5 - Multi-Transmitter Data Bus, Part 1-Technical Description●ARINC Report 629 Part 2-2 Multi-Transmitter Data Bus, Part 2-Application Guide●ARINC Report 631-5 VHF Digital Link (VDL) Mode 2 ImplementationProvisions●ARINC Report 631-6 VHF Digital Link (VDL) Mode 2 ImplementationProvisions Standards●ARINC Report 634 HF Data Link System Design Guidance Material●ARINC Report 635-4 HF Data Link Protocols●ARINC Report 636 Onboard Local Area Network (OLAN).Journals and websites/av/ AviationToday/technologies/ view/Avionics Journal of Military Electronics and ComputingSEB180512 G:\WORDPROC\LT\PD\BTEC LEVEL 6 DIPLOMAS\ENGINEERING\UNITS\PD031360 UNIT 22 AVIONIC SYSTEMS ENGINEERING.DOC.1-9/0。