Learning Control Book

Learning Control


  • Author : Dan Zhang
  • Publisher : Elsevier
  • Release Date : 2020-12-05
  • Genre: Technology & Engineering
  • Pages : 280
  • ISBN 10 : 9780128223154

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Learning Control Excerpt :

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

Machine Learning Control     Taming Nonlinear Dynamics and Turbulence Book
Score: 5
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Machine Learning Control Taming Nonlinear Dynamics and Turbulence


  • Author : Thomas Duriez
  • Publisher : Springer
  • Release Date : 2016-11-02
  • Genre: Technology & Engineering
  • Pages : 211
  • ISBN 10 : 9783319406244

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Machine Learning Control Taming Nonlinear Dynamics and Turbulence Excerpt :

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.

Data Driven Iterative Learning Control for Discrete Time Systems Book

Data Driven Iterative Learning Control for Discrete Time Systems


  • Author : Ronghu Chi
  • Publisher : Springer Nature
  • Release Date : 2022-12-17
  • Genre: Technology & Engineering
  • Pages : 239
  • ISBN 10 : 9789811959509

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Data Driven Iterative Learning Control for Discrete Time Systems Excerpt :

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 Book

Iterative Learning Control for Multi agent Systems Coordination


  • Author : Shiping Yang
  • Publisher : John Wiley & Sons
  • Release Date : 2017-03-03
  • Genre: Technology & Engineering
  • Pages : 272
  • ISBN 10 : 9781119189060

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Iterative Learning Control for Multi agent Systems Coordination Excerpt :

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

Self Learning Control of Finite Markov Chains Book

Self Learning Control of Finite Markov Chains


  • Author : A.S. Poznyak
  • Publisher : CRC Press
  • Release Date : 2000-01-03
  • Genre: Technology & Engineering
  • Pages : 318
  • ISBN 10 : 082479429X

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Self Learning Control of Finite Markov Chains Excerpt :

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.

Learning Control Book

Learning Control


  • Author : William Charles Messner
  • Publisher : Unknown
  • Release Date : 1992
  • Genre: Uncategoriezed
  • Pages : 270
  • ISBN 10 : UCAL:C3369545

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Learning Control Excerpt :

Real time Iterative Learning Control Book

Real time Iterative Learning Control


  • Author : Jian-Xin Xu
  • Publisher : Springer Science & Business Media
  • Release Date : 2008-12-12
  • Genre: Technology & Engineering
  • Pages : 194
  • ISBN 10 : 9781848821750

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Real time Iterative Learning Control Excerpt :

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.

Reinforcement Learning and Optimal Control Book

Reinforcement Learning and Optimal Control


  • Author : Dimitri Bertsekas
  • Publisher : Athena Scientific
  • Release Date : 2019-07-01
  • Genre: Computers
  • Pages : 388
  • ISBN 10 : 9781886529397

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Reinforcement Learning and Optimal Control Excerpt :

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 requireme

Iterative Learning Control Book

Iterative Learning Control


  • Author : David H. Owens
  • Publisher : Springer
  • Release Date : 2015-10-31
  • Genre: Technology & Engineering
  • Pages : 456
  • ISBN 10 : 9781447167723

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Iterative Learning Control Excerpt :

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.

Automation and Control Book

Automation and Control


  • Author : Constantin Volosencu
  • Publisher : BoD – Books on Demand
  • Release Date : 2021-04-21
  • Genre: Technology & Engineering
  • Pages : 422
  • ISBN 10 : 9781839627132

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Automation and Control Excerpt :

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.

Iterative Learning Control Book

Iterative Learning Control


  • Author : Zeungnam Bien
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-12-06
  • Genre: Technology & Engineering
  • Pages : 373
  • ISBN 10 : 9781461556299

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Iterative Learning Control Excerpt :

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.

European Control Conference 1993 Book

European Control Conference 1993


  • Author : Anonim
  • Publisher : European Control Association
  • Release Date : 1993-06-28
  • Genre: Uncategoriezed
  • Pages : 612
  • ISBN 10 : 978186723xxxx

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European Control Conference 1993 Excerpt :

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

Bio Inspired Collaborative Intelligent Control and Optimization Book

Bio Inspired Collaborative Intelligent Control and Optimization


  • Author : Yongsheng Ding
  • Publisher : Springer
  • Release Date : 2017-11-06
  • Genre: Technology & Engineering
  • Pages : 474
  • ISBN 10 : 9789811066894

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Bio Inspired Collaborative Intelligent Control and Optimization Excerpt :

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.

Iterative Learning Control for Deterministic Systems Book

Iterative Learning Control for Deterministic Systems


  • Author : Kevin L. Moore
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-12-06
  • Genre: Technology & Engineering
  • Pages : 152
  • ISBN 10 : 9781447119128

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Iterative Learning Control for Deterministic Systems Excerpt :

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.