Download Learning Control Ebook PDF

Learning Control

Learning Control
Applications in Robotics and Complex Dynamical Systems

by Dan Zhang,Bin Wei

  • Publisher : Elsevier
  • Release : 2020-12-05
  • Pages : 280
  • ISBN : 0128223154
  • Language : En, Es, Fr & De
GET BOOK

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
Analysis, Design, Integration and Applications

by Zeungnam Bien,Jian-Xin Xu

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 373
  • ISBN : 1461556295
  • Language : En, Es, Fr & De
GET BOOK

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.

Iterative Learning Control

Iterative Learning Control
An Optimization Paradigm

by David H. Owens

  • Publisher : Springer
  • Release : 2015-10-31
  • Pages : 456
  • ISBN : 1447167724
  • Language : En, Es, Fr & De
GET BOOK

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.

Iterative Learning Control for Systems with Iteration-Varying Trial Lengths

Iterative Learning Control for Systems with Iteration-Varying Trial Lengths
Synthesis and Analysis

by Dong Shen,Xuefang Li

  • Publisher : Springer
  • Release : 2019-01-29
  • Pages : 256
  • ISBN : 9811361363
  • Language : En, Es, Fr & De
GET BOOK

This book presents a comprehensive and detailed study on iterative learning control (ILC) for systems with iteration-varying trial lengths. Instead of traditional ILC, which requires systems to repeat on a fixed time interval, this book focuses on a more practical case where the trial length might randomly vary from iteration to iteration. The iteration-varying trial lengths may be different from the desired trial length, which can cause redundancy or dropouts of control information in ILC, making ILC design a challenging problem. The book focuses on the synthesis and analysis of ILC for both linear and nonlinear systems with iteration-varying trial lengths, and proposes various novel techniques to deal with the precise tracking problem under non-repeatable trial lengths, such as moving window, switching system, and searching-based moving average operator. It not only discusses recent advances in ILC for systems with iteration-varying trial lengths, but also includes numerous intuitive figures to allow readers to develop an in-depth understanding of the intrinsic relationship between the incomplete information environment and the essential tracking performance. This book is intended for academic scholars and engineers who are interested in learning about control, data-driven control, networked control systems, and related fields. It is also a useful resource for graduate students in the above field.

Iterative Learning Control with Passive Incomplete Information

Iterative Learning Control with Passive Incomplete Information
Algorithms Design and Convergence Analysis

by Dong Shen

  • Publisher : Springer
  • Release : 2018-04-16
  • Pages : 294
  • ISBN : 9811082677
  • Language : En, Es, Fr & De
GET BOOK

This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplete information, highlighting the incomplete input and output data resulting from practical factors such as data dropout, transmission disorder, communication delay, etc.—a cutting-edge topic in connection with the practical applications of ILC. It describes in detail three data dropout models: the random sequence model, Bernoulli variable model, and Markov chain model—for both linear and nonlinear stochastic systems. Further, it proposes and analyzes two major compensation algorithms for the incomplete data, namely, the intermittent update algorithm and successive update algorithm. Incomplete information environments include random data dropout, random communication delay, random iteration-varying lengths, and other communication constraints. With numerous intuitive figures to make the content more accessible, the book explores several potential solutions to this topic, ensuring that readers are not only introduced to the latest advances in ILC for systems with random factors, but also gain an in-depth understanding of the intrinsic relationship between incomplete information environments and essential tracking performance. It is a valuable resource for academics and engineers, as well as graduate students who are interested in learning about control, data-driven control, networked control systems, and related fields.

Real-time Iterative Learning Control

Real-time Iterative Learning Control
Design and Applications

by Jian-Xin Xu,Sanjib K. Panda,Tong Heng Lee

  • Publisher : Springer Science & Business Media
  • Release : 2008-12-12
  • Pages : 194
  • ISBN : 1848821751
  • Language : En, Es, Fr & De
GET BOOK

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.

Iterative Learning Control for Multi-agent Systems Coordination

Iterative Learning Control for Multi-agent Systems Coordination
A Book

by Shiping Yang,Jian-Xin Xu,Xuefang Li,Dong Shen

  • Publisher : John Wiley & Sons
  • Release : 2017-03-03
  • Pages : 272
  • ISBN : 1119189063
  • Language : En, Es, Fr & De
GET BOOK

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

Learning, Control and Hybrid Systems

Learning, Control and Hybrid Systems
Festschrift in Honor of Bruce Allen Francis and Mathukumalli Vidyasagar on the Occasion of Their 50th Birthdays

by Yutaka Yamamoto,Shinji Hara

  • Publisher : Springer
  • Release : 1999
  • Pages : 451
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

This volume covers a wide variety of papers on learning, control and hybrid systems. The papers are the proceedings of the Bangalore Workshop held in January 1998, that honoured the 50th birthdays of Mathukumalli Vidyasagar and Bruce Allen Francis. The 27 papers, written by international specialists in the field, cover a variety of interests. The editors have grouped these into the following 4 categories: a) Learning and Computational Issues b) Hybrid Systems c) Modeling, Identification and Estimation d) Robust Control. The research areas are diverse and comprehensive, giving the reader a unique opportunity to explore a variety of fields in which control/system theorists will be interested. Specific topics include: learning in dynamical systems, randomized algorithms in robust control design, identification of systems, digital signal processing, robust and H-infinity control, control of hybrid systems and supervisory control. This book will also provide readers with an insight into future directions in control and system theory, as they are predicted by experts from around the world. Readers will also benefit from being able share with the participants in the exciting new approaches to control and system theory that were discussed at the workshop. Future directions are predicted by experts from around the world.

Iterative Learning Control

Iterative Learning Control
Robustness and Monotonic Convergence for Interval Systems

by Hyo-Sung Ahn,Kevin L. Moore,YangQuan Chen

  • Publisher : Springer Science & Business Media
  • Release : 2007-06-28
  • Pages : 230
  • ISBN : 1846288592
  • Language : En, Es, Fr & De
GET BOOK

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.

Iterative Learning Control for Deterministic Systems

Iterative Learning Control for Deterministic Systems
A Book

by Kevin L. Moore

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 152
  • ISBN : 1447119126
  • Language : En, Es, Fr & De
GET BOOK

Iterative Learning Control for Deterministic Systems is part of the new Advances in Industrial Control series, edited by Professor M.J. Grimble and Dr. M.A. Johnson of the Industrial Control Unit, University of Strathclyde. 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.

Motor Learning and Control for Dance

Motor Learning and Control for Dance
Principles and Practices for Performers and Teachers

by Krasnow, Donna,Wilmerding, Mary Virginia

  • Publisher : Human Kinetics
  • Release : 2015-05-29
  • Pages : 336
  • ISBN : 145045741X
  • Language : En, Es, Fr & De
GET BOOK

Motor Learning and Control for Dance is the first textbook to blend dance science, somatic practices, and pedagogy and address motor learning theory from a dance perspective. It focuses on motor development, motor control, and motor learning while showcasing principles and practices for students and teachers.

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence
A Book

by Thomas Duriez,Steven L. Brunton,Bernd R. Noack

  • Publisher : Springer
  • Release : 2016-11-02
  • Pages : 211
  • ISBN : 3319406248
  • Language : En, Es, Fr & De
GET BOOK

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 for Industrial Robots with End Effector Sensing

Iterative Learning Control for Industrial Robots with End Effector Sensing
A Book

by Kiyonori Inaba

  • Publisher : Proquest, UMI Dissertation Publishing
  • Release : 2011-09
  • Pages : 174
  • ISBN : 9781243999443
  • Language : En, Es, Fr & De
GET BOOK

This dissertation considers precise Tool Center Point (TCP) tracking for industrial robots, i.e. tracking of reference trajectories in the Cartesian-space by the center point of a tool at the end effector of a robot. The difficulty of the control are due to disturbances and uncertainties in the reducers, such as non-linear friction, backlash, transmission error, and flexibility of the reducers. Under an assumption that a robot repeat the same tracking task repeatedly, we consider Iterative Learning Control (ILC) to accomplish precise TPC tracking. We focus on the frequency domain design of ILC and we develop a systematic design method for ILC based on Hinfinity synthesis. First we present the LFT (Linear Fraction Transformation)-based ILC design method, which was originally proposed for control of wafer scanners. We extend the original design to be able to apply non-causal learning as well as causal learning. We also propose an LMI (Linear Matrix Inequality)-based ILC design method. This method utilizes zero-phase weighting functions and realizes low order controllers which ensure robustness to disturbances and uncertainties. The controller performance is verified by experiments on an industrial robot. We introduce link-side ILC for industrial robots, i.e. ILC utilizing the link-side measurement for tracking in the Cartesian-space by the LMI-based ILC. ILC may be applied to each joint of a robot separately: i.e. Single-Input Single-Output (SISO) ILC design. We also consider Multiple-Input Multiple-Output (MIMO) ILC design, since Cartesian-space motion is a composite of multiple-joint motions. We divide the discussion of the link-side MIMO ILC in the Cartesian-space into three parts. First is the investigation of link-side ILC for a single-joint model. Secondly, we introduce MIMO ILC in the joint-space to compare the performance of SISO ILC and MIMO ILC. The last part is the investigation of ILC in the Cartesian-space which involves inverse kinematics of robots. All the ILC designs are evaluated by simulations. The third part of this dissertation is on vision-based ILC, which utilizes vision data as link-side measurement. The vision sensors provide the error between the TCP position and the target reference, but, they do not provide the TCP position itself. In this part, we estimate the desired link position to cancel the error in Cartesian-space by utilizing the kinematic relationship between the joint angle and the vision data. The controller performance is verified on a industrial robot.

Iterative Learning Control

Iterative Learning Control
Convergence, Robustness and Applications

by Yangquan Chen,Changyun Wen

  • Publisher : Springer
  • Release : 2007-10-03
  • Pages : 204
  • ISBN : 1846285399
  • Language : En, Es, Fr & De
GET BOOK

This book provides readers with a comprehensive coverage of iterative learning control. The book can be used as a text or reference for a course at graduate level and is also suitable for self-study and for industry-oriented courses of continuing education. Ranging from aerodynamic curve identification robotics to functional neuromuscular stimulation, Iterative Learning Control (ILC), started in the early 80s, is found to have wide applications in practice. Generally, a system under control may have uncertainties in its dynamic model and its environment. One attractive point in ILC lies in the utilisation of the system repetitiveness to reduce such uncertainties and in turn to improve the control performance by operating the system repeatedly. This monograph emphasises both theoretical and practical aspects of ILC. It provides some recent developments in ILC convergence and robustness analysis. The book also considers issues in ILC design. Several practical applications are presented to illustrate the effectiveness of ILC. The applied examples provided in this monograph are particularly beneficial to readers who wish to capitalise the system repetitiveness to improve system control performance.

Neural Adaptive Control Technology

Neural Adaptive Control Technology
A Book

by Rafa? ?bikowski,Kenneth J. Hunt

  • Publisher : World Scientific
  • Release : 1996
  • Pages : 347
  • ISBN : 9789810225575
  • Language : En, Es, Fr & De
GET BOOK

This book is an outgrowth of the workshop on Neural Adaptive Control Technology, NACT I, held in 1995 in Glasgow. Selected workshop participants were asked to substantially expand and revise their contributions to make them into full papers.The workshop was organised in connection with a three-year European Union funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland). A major aim of the NACT project is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from Daimler-Benz.In the book emphasis is put on development of sound theory of neural adaptive control for nonlinear control systems, but firmly anchored in the engineering context of industrial practice. Therefore the contributors are both renowned academics and practitioners from major industrial users of neurocontrol.

Self-Learning Control of Finite Markov Chains

Self-Learning Control of Finite Markov Chains
A Book

by A.S. Poznyak,Kaddour Najim,E. Gomez-Ramirez

  • Publisher : CRC Press
  • Release : 2000-01-03
  • Pages : 314
  • ISBN : 9780824794293
  • Language : En, Es, Fr & De
GET BOOK

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.

Sampled‐data Iterative Learning Control for Continuous‐time Nonlinear Systems with Iteration‐varying Lengths

Sampled‐data Iterative Learning Control for Continuous‐time Nonlinear Systems with Iteration‐varying Lengths
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2018
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

In this work, sampled‐data iterative learning control (ILC) method is extended to a class of continuous‐time nonlinear systems with iteration‐varying trial lengths. In order to propose a unified ILC algorithm, the tracking errors will be redefined when the trial length is shorter or longer than the desired one. Based on the modified tracking errors, 2 sampled‐data ILC schemes are proposed to handle the randomly varying trial lengths. Sufficient conditions are derived rigorously to guarantee the convergence of the nonlinear system at each sampling instant. To verify the effectiveness of the proposed ILC laws, simulations for a nonlinear system are performed. The simulation results show that if the sampling period is set to be small enough, the convergence of the learning algorithms can be achieved as the iteration number increases.

Iterative Learning Control for Flexible Structures

Iterative Learning Control for Flexible Structures
A Book

by Tingting Meng,Wei He

  • Publisher : Springer Nature
  • Release : 2020-03-23
  • Pages : 182
  • ISBN : 9811527849
  • Language : En, Es, Fr & De
GET BOOK

This book presents iterative learning control (ILC) to address practical issues of flexible structures. It is divided into four parts: Part I provides a general introduction to ILC and flexible structures, while Part II proposes various types of ILC for simple flexible structures to address issues such as vibration, input saturation, input dead-zone, input backlash, external disturbances, and trajectory tracking. It also includes simple partial differential equations to deal with the common problems of flexible structures. Part III discusses the design of ILC for flexible micro aerial vehicles and two-link manipulators, and lastly, Part IV offers a summary of the topics covered. Unlike most of the literature on ILC, which focuses on ordinary differential equation systems, this book explores distributed parameter systems, which are comparatively less stabilized through ILC.Including a comprehensive introduction to ILC of flexible structures, it also examines novel approaches used in ILC to address input constraints and disturbance rejection. This book is intended for researchers, graduate students and engineers in various fields, such as flexible structures, external disturbances, nonlinear inputs and tracking control.

Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation

Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation
A Book

by Chris T. Freeman,Eric Rogers,Jane H. Burridge,Ann-Marie Hughes,Katie L. Meadmore

  • Publisher : Springer
  • Release : 2015-06-25
  • Pages : 124
  • ISBN : 1447167260
  • Language : En, Es, Fr & De
GET BOOK

Iterative learning control (ILC) has its origins in the control of processes that perform a task repetitively with a view to improving accuracy from trial to trial by using information from previous executions of the task. This brief shows how a classic application of this technique – trajectory following in robots – can be extended to neurological rehabilitation after stroke. Regaining upper limb movement is an important step in a return to independence after stroke, but the prognosis for such recovery has remained poor. Rehabilitation robotics provides the opportunity for repetitive task-oriented movement practice reflecting the importance of such intense practice demonstrated by conventional therapeutic research and motor learning theory. Until now this technique has not allowed feedback from one practice repetition to influence the next, also implicated as an important factor in therapy. The authors demonstrate how ILC can be used to adjust external functional electrical stimulation of patients’ muscles while they are repeatedly performing a task in response to the known effects of stimulation in previous repetitions. As the motor nerves and muscles of the arm reaquire the ability to convert an intention to move into a motion of accurate trajectory, force and rapidity, initially intense external stimulation can now be scaled back progressively until the fullest possible independence of movement is achieved.

Control and Intelligent Systems

Control and Intelligent Systems
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2003
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK