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Iterative Learning Control

Iterative Learning Control
  • Author : David H. Owens
  • Publisher :Unknown
  • Release Date :2015-10-31
  • Total pages :456
  • ISBN : 9781447167723
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Summary : 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
  • Author : Dong Shen,Xuefang Li
  • Publisher :Unknown
  • Release Date :2019-01-29
  • Total pages :256
  • ISBN : 9789811361364
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Summary : 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
  • Author : Dong Shen
  • Publisher :Unknown
  • Release Date :2018-04-16
  • Total pages :294
  • ISBN : 9789811082672
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Summary : 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
  • Author : Jian-Xin Xu,Sanjib K. Panda,Tong Heng Lee
  • Publisher :Unknown
  • Release Date :2008-12-12
  • Total pages :194
  • ISBN : 9781848821750
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Summary : 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
  • Author : Shiping Yang,Jian-Xin Xu,Xuefang Li,Dong Shen
  • Publisher :Unknown
  • Release Date :2017-03-03
  • Total pages :272
  • ISBN : 9781119189060
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Summary : 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
  • Author : Hyo-Sung Ahn,Kevin L. Moore,YangQuan Chen
  • Publisher :Unknown
  • Release Date :2007-06-28
  • Total pages :230
  • ISBN : 9781846288593
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Summary : 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.

Self-Learning Control of Finite Markov Chains

Self-Learning Control of Finite Markov Chains
  • Author : A.S. Poznyak,Kaddour Najim,E. Gomez-Ramirez
  • Publisher :Unknown
  • Release Date :2000-01-03
  • Total pages :314
  • ISBN : 082479429X
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Summary : 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.

Practical Iterative Learning Control with Frequency Domain Design and Sampled Data Implementation

Practical Iterative Learning Control with Frequency Domain Design and Sampled Data Implementation
  • Author : Danwei Wang,Yongqiang Ye,Bin Zhang
  • Publisher :Unknown
  • Release Date :2014-06-19
  • Total pages :226
  • ISBN : 9789814585606
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Summary : This book is on the iterative learning control (ILC) with focus on the design and implementation. We approach the ILC design based on the frequency domain analysis and address the ILC implementation based on the sampled data methods. This is the first book of ILC from frequency domain and sampled data methodologies. The frequency domain design methods offer ILC users insights to the convergence performance which is of practical benefits. This book presents a comprehensive framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between learning performance and learning stability. The sampled data implementation ensures effective execution of ILC in practical dynamic systems. The presented sampled data ILC methods also ensure the balance of performance and stability of learning process. Furthermore, the presented theories and methodologies are tested with an ILC controlled robotic system. The experimental results show that the machines can work in much higher accuracy than a feedback control alone can offer. With the proposed ILC algorithms, it is possible that machines can work to their hardware design limits set by sensors and actuators. The target audience for this book includes scientists, engineers and practitioners involved in any systems with repetitive operations.

Recent Advances in Learning and Control

Recent Advances in Learning and Control
  • Author : Vincent D. Blondel,Stephen P. Boyd,Hidenori Kimura
  • Publisher :Unknown
  • Release Date :2008-01-11
  • Total pages :282
  • ISBN : 9781848001541
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Summary : This volume is composed of invited papers on learning and control. The contents form the proceedings of a workshop held in January 2008, in Hyderabad that honored the 60th birthday of Doctor Mathukumalli Vidyasagar. The 14 papers, written by international specialists in the field, cover a variety of interests within the broader field of learning and control. The diversity of the research provides a comprehensive overview of a field of great interest to control and system theorists.

Learning Control

Learning Control
  • Author : Dan Zhang,Bin Wei
  • Publisher :Unknown
  • Release Date :2021-02-05
  • Total pages :280
  • ISBN : 9780128223147
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Summary : 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

Neural Adaptive Control Technology

Neural Adaptive Control Technology
  • Author : Rafa? ?bikowski,Kenneth J. Hunt
  • Publisher :Unknown
  • Release Date :1996
  • Total pages :347
  • ISBN : 9810225571
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Summary : 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.

Deterministic Learning Theory for Identification, Recognition, and Control

Deterministic Learning Theory for Identification, Recognition, and Control
  • Author : Cong Wang,David J. Hill
  • Publisher :Unknown
  • Release Date :2018-10-03
  • Total pages :207
  • ISBN : 9781420007763
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Summary : Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way. A Deterministic View of Learning in Dynamic Environments The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems. A New Model of Information Processing This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).

International Conference on Electrical, Control and Automation (ICECA 2014)

International Conference on Electrical, Control and Automation (ICECA 2014)
  • Author : Samson YU
  • Publisher :Unknown
  • Release Date :2014-02-04
  • Total pages :900
  • ISBN : 9781605951522
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Summary : International Conference on Electrical, Control and Automation (ICECA 2014)will be held from February 22nd to 23rd, 2014 in Shanghai, China. CECA 2014 will bring together top researchers from Asian Pacific areas, North America, Europe and around the world to exchange research results and address open issues in all aspects of Electrical, Control and Automation. The ICECA 2014 welcomes the submission of original full research papers, short papers, posters, workshop proposals, tutorials, and industrial professional reports.

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence
  • Author : Thomas Duriez,Steven L. Brunton,Bernd R. Noack
  • Publisher :Unknown
  • Release Date :2016-11-02
  • Total pages :211
  • ISBN : 9783319406244
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Summary : 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.

Control and Constraint in E-Learning: Choosing When to Choose

Control and Constraint in E-Learning: Choosing When to Choose
  • Author : Dron, Jon
  • Publisher :Unknown
  • Release Date :2007-03-31
  • Total pages :366
  • ISBN : 9781599043920
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Summary : "This book unifies and synthesizes an assortment of theories about learner control, autonomy, self-direction, adult learning for educationalists, e-learning practitioners and e-learning developers; it provides a theoretical approach to building computer systems to support adults learning via the Internet, existing e-learning environments and how they should be used, and the process of education in general"--Provided by publisher.

Information Processing in Motor Control and Learning

Information Processing in Motor Control and Learning
  • Author : George E. Stelmach
  • Publisher :Unknown
  • Release Date :2014-06-28
  • Total pages :328
  • ISBN : 9781483268521
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Summary : Information Processing in Motor Control and Learning provides the theoretical ideas and experimental findings in the field of motor behavior research. The text presents a balanced combination of theory and empirical data. Chapters discuss several theoretical issues surrounding skill acquisition; motor programming; and the nature and significance of preparation, rapid movement sequences, attentional demands, and sensorimotor integration in voluntary movements. The book will be interesting to psychologists, neurophysiologists, and graduate students in related fields.

Intelligent Adaptive Control

Intelligent Adaptive Control
  • Author : Lakhmi C. Jain,Clarence W. de Silva
  • Publisher :Unknown
  • Release Date :1998-12-29
  • Total pages :440
  • ISBN : 0849398053
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Summary : This book describes important techniques, developments, and applications of computational intelligence in system control. Chapters present: an introduction to the fundamentals of neural networks, fuzzy logic, and evolutionary computing a rigorous treatment of intelligent control industrial applications of intelligent control and soft computing, including transportation, petroleum, motor drive, industrial automation, and fish processing other knowledge-based techniques, including vehicle driving aid and air traffic management Intelligent Adaptive Control provides a state-of-the-art treatment of practical applications of computational intelligence in system control. The book cohesively covers introductory and advanced theory, design, implementation, and industrial use - serving as a singular resource for the theory and application of intelligent control, particularly employing fuzzy logic, neural networks, and evolutionary computing.

Design of Intelligent Control Systems Based on Hierarchical Stochastic Automata

Design of Intelligent Control Systems Based on Hierarchical Stochastic Automata
  • Author : Pedro U. Lima,George N. Saridis
  • Publisher :Unknown
  • Release Date :1996
  • Total pages :154
  • ISBN : 9810222556
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Summary : In recent years works done by most researchers towards building autonomous intelligent controllers frequently mention the need for a methodology of design and a measure of how successful the final result is. This monograph introduces a design methodology for intelligent controllers based on the analytic theory of intelligent machines introduced by Saridis in the 1970s. The methodology relies on the existing knowledge about designing the different sub-systems composing an intelligent machine. Its goal is to provide a performance measure applicable to any of the sub-systems, and use that measure to learn on-line the best among the set of pre-designed alternatives, given the state of the environment where the machine operates. Different designs can be compared using this novel approach.

High-Level Feedback Control with Neural Networks

High-Level Feedback Control with Neural Networks
  • Author : Young Ho Kim,Frank L. Lewis
  • Publisher :Unknown
  • Release Date :1998
  • Total pages :216
  • ISBN : 9810233760
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Summary : Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively "add intelligence" to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty. This book bridges the gap between feedback control and AI. It provides design techniques for "high-level" neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including "dynamic output feedback", "reinforcement learning" and "optimal design", as well as a "fuzzy-logic reinforcement" controller. The control topologies areintuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.

Nonlinear Control of Engineering Systems

Nonlinear Control of Engineering Systems
  • Author : Warren E. Dixon,Aman Behal,Darren M. Dawson,Siddharth P. Nagarkatti
  • Publisher :Unknown
  • Release Date :2003-06-26
  • Total pages :394
  • ISBN : 081764265X
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Summary : This practical yet rigorous book provides a development of nonlinear, Lyapunov-based tools and their use in the solution of control-theoretic problems. Rich in motivating examples and new design techniques, the text balances theoretical foundations and real-world implementation.

Advances in Neural Networks - ISNN 2007

Advances in Neural Networks - ISNN 2007
  • Author : Derong Liu,Shumin Fei,Zeng-Guang Hou,Changyin Sun,Huaguang Zhang
  • Publisher :Unknown
  • Release Date :2007-05-24
  • Total pages :1210
  • ISBN : 9783540723943
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Summary : The three volume set LNCS 4491/4492/4493 constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. The 262 revised long papers and 192 revised short papers presented were carefully reviewed and selected from a total of 1.975 submissions. The papers are organized in topical sections on neural fuzzy control, neural networks for control applications, adaptive dynamic programming and reinforcement learning, neural networks for nonlinear systems modeling, robotics, stability analysis of neural networks, learning and approximation, data mining and feature extraction, chaos and synchronization, neural fuzzy systems, training and learning algorithms for neural networks, neural network structures, neural networks for pattern recognition, SOMs, ICA/PCA, biomedical applications, feedforward neural networks, recurrent neural networks, neural networks for optimization, support vector machines, fault diagnosis/detection, communications and signal processing, image/video processing, and applications of neural networks.