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Learning Control

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Learning Control

Learning Control Book
Author : Dan Zhang,Bin Wei
Publisher : Elsevier
Release : 2020-12-05
ISBN : 0128223154
Language : En, Es, Fr & De

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Book Description :

Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length. Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems Demonstrates computational techniques for control systems Covers iterative learning impedance control in both human-robot interaction and collaborative robots

Iterative Learning Control

Iterative Learning Control Book
Author : David H. Owens
Publisher : Springer
Release : 2015-10-31
ISBN : 1447167724
Language : En, Es, Fr & De

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Book Description :

This book develops a coherent and quite general theoretical approach to algorithm design for iterative learning control based on the use of operator representations and quadratic optimization concepts including the related ideas of inverse model control and gradient-based design. Using detailed examples taken from linear, discrete and continuous-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately as their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates the underlying robustness of the paradigm and also includes new control laws that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference and auxiliary signals and also to support new properties such as spectral annihilation. Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.

Machine Learning Control Taming Nonlinear Dynamics and Turbulence

Machine Learning Control     Taming Nonlinear Dynamics and Turbulence Book
Author : Thomas Duriez,Steven L. Brunton,Bernd R. Noack
Publisher : Springer
Release : 2016-11-02
ISBN : 3319406248
Language : En, Es, Fr & De

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Book Description :

This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.

Iterative Learning Control

Iterative Learning Control Book
Author : Zeungnam Bien,Jian-Xin Xu
Publisher : Springer Science & Business Media
Release : 2012-12-06
ISBN : 1461556295
Language : En, Es, Fr & De

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Book Description :

Iterative Learning Control (ILC) differs from most existing control methods in the sense that, it exploits every possibility to incorporate past control informa tion, such as tracking errors and control input signals, into the construction of the present control action. There are two phases in Iterative Learning Control: first the long term memory components are used to store past control infor mation, then the stored control information is fused in a certain manner so as to ensure that the system meets control specifications such as convergence, robustness, etc. It is worth pointing out that, those control specifications may not be easily satisfied by other control methods as they require more prior knowledge of the process in the stage of the controller design. ILC requires much less information of the system variations to yield the desired dynamic be haviors. Due to its simplicity and effectiveness, ILC has received considerable attention and applications in many areas for the past one and half decades. Most contributions have been focused on developing new ILC algorithms with property analysis. Since 1992, the research in ILC has progressed by leaps and bounds. On one hand, substantial work has been conducted and reported in the core area of developing and analyzing new ILC algorithms. On the other hand, researchers have realized that integration of ILC with other control techniques may give rise to better controllers that exhibit desired performance which is impossible by any individual approach.

Machine Learning Control by Symbolic Regression

Machine Learning Control by Symbolic Regression Book
Author : Askhat Diveev,Elizaveta Shmalko
Publisher : Springer Nature
Release : 2021-10-23
ISBN : 3030832139
Language : En, Es, Fr & De

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Book Description :

This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting; the search for new laws in economics, politics, sociology. Accumulating many years of experience in the development and application of numerical methods of symbolic regression to solving control problems, the authors offer new possibilities not only in the field of control automation, but also in the design of completely different optimal structures in many fields. For specialists in the field of control, Machine Learning Control by Symbolic Regression opens up a new promising direction of research and acquaints scientists with the methods of automatic construction of control systems.For specialists in the field of machine learning, the book opens up a new, much broader direction than neural networks: methods of symbolic regression. This book makes it easy to master this new area in machine learning and apply this approach everywhere neural networks are used. For mathematicians, the book opens up a new approach to the construction of numerical methods for obtaining analytical solutions to unsolvable problems; for example, numerical analytical solutions of algebraic equations, differential equations, non-trivial integrals, etc. For specialists in the field of artificial intelligence, the book offers a machine way to solve problems, framed in the form of analytical relationships.

Data Driven Iterative Learning Control for Discrete Time Systems

Data Driven Iterative Learning Control for Discrete Time Systems Book
Author : Ronghu Chi,Yu Hui,Zhongsheng Hou
Publisher : Springer Nature
Release : 2022-12-17
ISBN : 9811959501
Language : En, Es, Fr & De

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Book Description :

This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

Iterative Learning Control for Multi agent Systems Coordination

Iterative Learning Control for Multi agent Systems Coordination Book
Author : Shiping Yang,Jian-Xin Xu,Xuefang Li,Dong Shen
Publisher : John Wiley & Sons
Release : 2017-03-03
ISBN : 1119189063
Language : En, Es, Fr & De

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Book Description :

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, showcasing recent advances and industrially relevant applications Explores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS) Concisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks and control processes Covers basic theory, rigorous mathematics as well as engineering practice

Iterative Learning Control

Iterative Learning Control Book
Author : Hyo-Sung Ahn,Kevin L. Moore,YangQuan Chen
Publisher : Springer Science & Business Media
Release : 2007-06-28
ISBN : 1846288592
Language : En, Es, Fr & De

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Book Description :

This monograph studies the design of robust, monotonically-convergent iterative learning controllers for discrete-time systems. It presents a unified analysis and design framework that enables designers to consider both robustness and monotonic convergence for typical uncertainty models, including parametric interval uncertainties, iteration-domain frequency uncertainty, and iteration-domain stochastic uncertainty. The book shows how to use robust iterative learning control in the face of model uncertainty.

Reinforcement Learning and Optimal Control

Reinforcement Learning and Optimal Control Book
Author : Dimitri Bertsekas
Publisher : Athena Scientific
Release : 2019-07-01
ISBN : 1886529396
Language : En, Es, Fr & De

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Book Description :

This book considers large and challenging multistage decision problems, which can be solved in principle by dynamic programming (DP), but their exact solution is computationally intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are collectively known by several essentially equivalent names: reinforcement learning, approximate dynamic programming, neuro-dynamic programming. They have been at the forefront of research for the last 25 years, and they underlie, among others, the recent impressive successes of self-learning in the context of games such as chess and Go. Our subject has benefited greatly from the interplay of ideas from optimal control and from artificial intelligence, as it relates to reinforcement learning and simulation-based neural network methods. One of the aims of the book is to explore the common boundary between these two fields and to form a bridge that is accessible by workers with background in either field. Another aim is to organize coherently the broad mosaic of methods that have proved successful in practice while having a solid theoretical and/or logical foundation. This may help researchers and practitioners to find their way through the maze of competing ideas that constitute the current state of the art. This book relates to several of our other books: Neuro-Dynamic Programming (Athena Scientific, 1996), Dynamic Programming and Optimal Control (4th edition, Athena Scientific, 2017), Abstract Dynamic Programming (2nd edition, Athena Scientific, 2018), and Nonlinear Programming (Athena Scientific, 2016). However, the mathematical style of this book is somewhat different. While we provide a rigorous, albeit short, mathematical account of the theory of finite and infinite horizon dynamic programming, and some fundamental approximation methods, we rely more on intuitive explanations and less on proof-based insights. Moreover, our mathematical requirements are quite modest: calculus, a minimal use of matrix-vector algebra, and elementary probability (mathematically complicated arguments involving laws of large numbers and stochastic convergence are bypassed in favor of intuitive explanations). The book illustrates the methodology with many examples and illustrations, and uses a gradual expository approach, which proceeds along four directions: (a) From exact DP to approximate DP: We first discuss exact DP algorithms, explain why they may be difficult to implement, and then use them as the basis for approximations. (b) From finite horizon to infinite horizon problems: We first discuss finite horizon exact and approximate DP methodologies, which are intuitive and mathematically simple, and then progress to infinite horizon problems. (c) From deterministic to stochastic models: We often discuss separately deterministic and stochastic problems, since deterministic problems are simpler and offer special advantages for some of our methods. (d) From model-based to model-free implementations: We first discuss model-based implementations, and then we identify schemes that can be appropriately modified to work with a simulator. The book is related and supplemented by the companion research monograph Rollout, Policy Iteration, and Distributed Reinforcement Learning (Athena Scientific, 2020), which focuses more closely on several topics related to rollout, approximate policy iteration, multiagent problems, discrete and Bayesian optimization, and distributed computation, which are either discussed in less detail or not covered at all in the present book. The author's website contains class notes, and a series of videolectures and slides from a 2021 course at ASU, which address a selection of topics from both books.

Learning Control

Learning Control Book
Author : William Charles Messner
Publisher : Unknown
Release : 1992
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Learning Control book written by William Charles Messner, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Motor Learning and Control for Practitioners

Motor Learning and Control for Practitioners Book
Author : Cheryl A. Coker
Publisher : Routledge
Release : 2017-09-22
ISBN : 1351734628
Language : En, Es, Fr & De

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Book Description :

With an array of critical and engaging pedagogical features, the fourth edition of Motor Learning and Control for Practitioners offers the best practical introduction to motor learning available. This reader-friendly text approaches motor learning in accessible and simple terms, and lays a theoretical foundation for assessing performance; providing effective instruction; and designing practice, rehabilitation, and training experiences that promote skill acquisition. Features such as Exploration Activities and Cerebral Challenges involve students at every stage, while a broad range of examples helps readers put theory into practice. The book also provides access to a fully updated companion website, which includes laboratory exercises, an instructors’ manual, a test bank, and lecture slides. As a complete resource for teaching an evidence-based approach to practical motor learning, this is an essential text for practitioners and students who plan to work in physical education, kinesiology, exercise science, coaching, physical therapy, or dance.

Linear and Nonlinear Iterative Learning Control

Linear and Nonlinear Iterative Learning Control Book
Author : Jian-Xin Xu,Ying Tan
Publisher : Springer
Release : 2003-09-04
ISBN : 3540448454
Language : En, Es, Fr & De

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Book Description :

This monograph summarizes the recent achievements made in the field of iterative learning control. The book is self-contained in theoretical analysis and can be used as a reference or textbook for a graduate level course as well as for self-study. It opens a new avenue towards a new paradigm in deterministic learning control theory accompanied by detailed examples.

Real time Iterative Learning Control

Real time Iterative Learning Control Book
Author : Jian-Xin Xu,Sanjib K. Panda,Tong Heng Lee
Publisher : Springer Science & Business Media
Release : 2008-12-12
ISBN : 1848821751
Language : En, Es, Fr & De

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Book Description :

Real-time Iterative Learning Control demonstrates how the latest advances in iterative learning control (ILC) can be applied to a number of plants widely encountered in practice. The book gives a systematic introduction to real-time ILC design and source of illustrative case studies for ILC problem solving; the fundamental concepts, schematics, configurations and generic guidelines for ILC design and implementation are enhanced by a well-selected group of representative, simple and easy-to-learn example applications. Key issues in ILC design and implementation in linear and nonlinear plants pervading mechatronics and batch processes are addressed, in particular: ILC design in the continuous- and discrete-time domains; design in the frequency and time domains; design with problem-specific performance objectives including robustness and optimality; design in a modular approach by integration with other control techniques; and design by means of classical tools based on Bode plots and state space.

Intelligent Control Principles Techniques and Applications

Intelligent Control  Principles  Techniques and Applications Book
Author : Anonim
Publisher : Unknown
Release : 2022-11-30
ISBN : 9814499323
Language : En, Es, Fr & De

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Book Description :

Download Intelligent Control Principles Techniques and Applications book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Iterative Learning Control for Deterministic Systems

Iterative Learning Control for Deterministic Systems Book
Author : Kevin L. Moore
Publisher : Springer Science & Business Media
Release : 2012-12-06
ISBN : 1447119126
Language : En, Es, Fr & De

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Book Description :

The material presented in this book addresses the analysis and design of learning control systems. It begins with an introduction to the concept of learning control, including a comprehensive literature review. The text follows with a complete and unifying analysis of the learning control problem for linear LTI systems using a system-theoretic approach which offers insight into the nature of the solution of the learning control problem. Additionally, several design methods are given for LTI learning control, incorporating a technique based on parameter estimation and a one-step learning control algorithm for finite-horizon problems. Further chapters focus upon learning control for deterministic nonlinear systems, and a time-varying learning controller is presented which can be applied to a class of nonlinear systems, including the models of typical robotic manipulators. The book concludes with the application of artificial neural networks to the learning control problem. Three specific ways to neural nets for this purpose are discussed, including two methods which use backpropagation training and reinforcement learning. The appendices in the book are particularly useful because they serve as a tutorial on artificial neural networks.

Handbook of Reinforcement Learning and Control

Handbook of Reinforcement Learning and Control Book
Author : Kyriakos G. Vamvoudakis,Yan Wan,Frank L. Lewis,Derya Cansever
Publisher : Springer Nature
Release : 2021-06-23
ISBN : 3030609901
Language : En, Es, Fr & De

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Book Description :

This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.

Automation and Control

Automation and Control Book
Author : Constantin Volosencu,Serdar Küçük,José Guerrero,Oscar Valero
Publisher : BoD – Books on Demand
Release : 2021-04-21
ISBN : 1839627131
Language : En, Es, Fr & De

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Book Description :

The book presents recent theoretical and practical information about the field of automation and control. It includes fifteen chapters that promote automation and control in practical applications in the following thematic areas: control theory, autonomous vehicles, mechatronics, digital image processing, electrical grids, artificial intelligence, and electric motor drives. The book also presents and discusses applications that improve the properties and performances of process control with examples and case studies obtained from real-world research in the field. Automation and Control is designed for specialists, engineers, professors, and students.

European Control Conference 1993

European Control Conference 1993 Book
Author : Anonim
Publisher : European Control Association
Release : 1993-06-28
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Proceedings of the European Control Conference 1993, Groningen, Netherlands, June 28 – July 1, 1993

Bio Inspired Collaborative Intelligent Control and Optimization

Bio Inspired Collaborative Intelligent Control and Optimization Book
Author : Yongsheng Ding,Lei Chen,Kuangrong Hao
Publisher : Springer
Release : 2017-11-06
ISBN : 9811066892
Language : En, Es, Fr & De

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Book Description :

This book presents state-of-the-art research advances in the field of biologically inspired cooperative control theories and their applications. It describes various biologically inspired cooperative control and optimization approaches and highlights real-world examples in complex industrial processes. Multidisciplinary in nature and closely integrating theory and practice, the book will be of interest to all university researchers, control engineers and graduate students in intelligent systems and control who wish to learn the core principles, methods, algorithms, and applications.

Self Learning Control of Finite Markov Chains

Self Learning Control of Finite Markov Chains Book
Author : A.S. Poznyak,Kaddour Najim,E. Gomez-Ramirez
Publisher : CRC Press
Release : 2018-10-03
ISBN : 1482273276
Language : En, Es, Fr & De

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Book Description :

Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.