Iterative Learning Control: Analysis, Design, Integration and Applications / Edition 1

by Zeungnam Bien, Jian-Xin Xu

Overview

Iterative Learning Control (ILC) differs from most existing control methods in the sense that, it exploits every possibility to incorporate past control information, 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 information, 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.

Product Details

ISBN-13:	9780792382133
Publisher:	Springer US
Publication date:	07/31/1998
Edition description:	1998
Pages:	373
Product dimensions:	6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

I General Introduction to Iterative Learning Control.- A Brief History of Iterative Learning Control.- The Frontiers of Iterative Learning Control.- II Property Analysis of Iterative Learning Control.- Robustness and Convergence of A PD-type Iterative Learning Controller.- Ability of Learning Comes from Passivity and Dissipativity of System Dynamics.- On the Iterative Learning Control of Sampled-Data Systems.- High-order Iterative Learning Control of Discrete-time Nonlinear Systems Using Current Iteration Tracking Error.- III The Design Issues of Iterative Learning Control.- Designing Iterative Learning and Repetitive Controllers.- Design of an ILC for Linear Systems with Time-Delay and Initial State Error.- Design of Quadratic Criterion-based Iterative Learning Control.- Robust ILC with Current Feedback for Uncertain Linear Systems.- IV Integration of Iterative Learning Control with other Intelligent Controls.- Model Reference Learning Control with a Wavelet Network.- Neural-based Iterative Learning Control.- Adaptive Learning Control of Robotic Systems and Its Extension to a Class of Nonlinear Systems.- Direct Learning Control of Non-uniform Trajectories.- System Identification and Learning Control.- V Implementations of Iterative Learning Control Method.- Model-Based Predictive Control Combined with Iterative Learning for Batch or Repetitive Processe.- Iterative Learning Control with Non-Standard Assumptions Applied to the Control of Gas-Metal Arc Welding.- Robust Control of Functional Neuromuscular Stimulation System by Discrete-time Iterative Learning.- About the Editors.

Editorial Reviews

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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