Iterative Learning Control: Analysis, Design, Integration and Applications

Overview

This book provides a comprehensive update and overview of iterative learning control theory and techniques relevant to industrial automation, and focuses on new research directions for the 21st century.
Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology. Iterative Learning Control: Analysis, Design, Integration and Applications provides dynamic coverage of ILC's history, its expanding real-world applications, ...

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Paperback (Softcover reprint of the original 1st ed. 1998)
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Overview

This book provides a comprehensive update and overview of iterative learning control theory and techniques relevant to industrial automation, and focuses on new research directions for the 21st century.
Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology. Iterative Learning Control: Analysis, Design, Integration and Applications provides dynamic coverage of ILC's history, its expanding real-world applications, and its robustness and convergence. Also included are sampled-data and discrete-time issues, design guidelines and quadratic criterion, the ability of dynamic systems to learn, time-delay problem, integration (with neural network, fuzzy logic and wavelet), direct learning, and identification, in addition to ILC's possible applications to batch and welding processes, neuromuscular stimulation, and other fast-changing fields.
The contributions are written by some of the leading internationally recognized researchers in ILC.
Iterative Learning Control: Analysis, Design, Integration and Applications will be of interest to researchers and engineers in robotics, automation, systems and control, and signal processing.

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Editorial Reviews

Booknews
Explores a control method that exploits every possibility to incorporate past control information, such as tracking errors and control input signals, into constructing a present control action. Summarizes the status of research, points out trends to keep an eye on or a hand in, and an invitation to practicing control engineers and academic researchers to consider the merits of the method in their own research and applications. The 18 papers cover the general background, properties of the approach, design issues, integration with other intelligent control methods, and implementation. Among the topics are the high-order control of discrete-time nonlinear systems using current iteration tracking error, designing iterative learning and repetitive controllers, model-reference learning control with a wavelet network, gas-metal arc welding, and functional neuromuscular simulation. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9781461375753
  • Publisher: Springer US
  • Publication date: 4/30/2013
  • Edition description: Softcover reprint of the original 1st ed. 1998
  • Edition number: 1
  • Pages: 373
  • Product dimensions: 6.10 (w) x 9.25 (h) x 0.83 (d)

Table of Contents

List of Figures. List of Tables. Preface. Part I: General Introduction to Iterative Learning Control. 1. A Brief History of Iterative Learning Control; S. Arimoto. 2. The Frontiers of Iterative Learning Control; Jian-Xin Xu, Zenn Z. Bien. Part II: Property Analysis of Iterative Learning Control. 3. Robustness and Convergence of a PD-type Iterative Learning Controller; Hak-Sung Lee, Zeungnam Bien. 4. Ability of Learning Comes from Passivity and Dissipativity of System Dynamics; S. Arimoto. 5. On the Iterative Learning Control of Sampled-Data Systems; Chiang-Ju Chien. 6. High-Order Iterative Learning Control of Discrete-Time Nonlinear Systems Using Current Iteration Tracking Error; Yangquan Chen, et al. Part III: The Design Issues of Iterative Learning Control. 7. Designing Iterative Learning and Repetitive Controllers; R.W. Longman. 8. Design of an ILC for Linear Systems with Time-Delay and Initial State Error; Kwang-Hyun Park, et al. 9. Design of Quadratic Criterion-Based Iterative Learning Control; Kwang Soon Lee, J.H. Lee. 10. Robust ILC with Current Feedback for Uncertain Linear Systems; Tae-Yong Doh, Myung Jin Chung. Part IV: Integration of Iterative Learning Control with Other Intelligent Controls. 11. Model Reference Learning Control with a Wavelet Network; M. Fukuda, S. Shin. 12. Neural-Based Iterative Learning Control; Jin Young Choi, et al. 13. Adaptive Learning Control of Robotic Systems and Its Extension to a Class of Nonlinear Systems; B.H. Park, et al. 14. Direct Learning Control of Non-Uniform Trajectories; Jian-Xin Xu, Yanbin Song. 15. System Identification and Learning Control; M.Q. Phan, J.A. Frueh. Part V: Implementations of Iterative Learning Control Method. 16. Model-Based Predictive Control Combined with Iterative Learning for Batch or Repetitive Processes; Kwang Soon Lee, J.H. Lee. 17. Iterative Learning Control with Non-Standard Assumptions Applied to the Control of Gas-Metal Arc Welding; K.L. Moore, A. Matheus. 18. Robust Control of Functional Neuromuscular Stimulation System by Discrete-time Iterative Learning; Huifang Dou, et al. Index. About the Editors.

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