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.
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.
Springer Book Archives