Название: Iterative Learning Control Algorithms and Experimental Benchmarking
Автор: Eric Rogers, Bing Chu, Christopher Freeman, Paul Lewin
Издательство: Wiley
Год: 2023
Страниц: 451
Язык: английский
Формат: pdf (true)
Размер: 21.3 MB

Iterative Learning Control Algorithms and Experimental Benchmarking Presents key cutting edge research into the use of iterative learning control.

The book discusses the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user. The book provides integrated coverage of the major approaches to-date in terms of basic systems, theoretic properties, design algorithms, and experimentally measured performance, as well as the links with repetitive control and other related areas.

The standard ILC design specification assumes that a reference trajectory is available. The difference between the output on any trial and the reference trajectory is the error on this trial. Then the task is to design a control law that forces the sequence of errors to converge from trial-to-trial, ideally to zero or to within an acceptable tolerance. The convergence of ILC designs is a very well-studied problem for various forms of dynamics. It has led to many design algorithms and, in many cases, at least laboratory-level supporting experimental evaluation.

Robotics is a source of many problems to which ILC can be applied. A particular example is “pick and place” operations, e.g. a robot required to collect an object from a given location, transfer it over a finite time, place it on a conveyor under synchronization, and then return to the starting location for the next one, and so on. The control input for the subsequent trial can be computed during the reset time, with all of the previous trial error data available if required since the data are generated on the previous trial. In this application, error convergence in the trial variable must be supported by control of the dynamics generated along the trials, e.g. to prevent unacceptable behavior, such as oscillations that would be of particular concern should the object be an open-necked container containing a liquid.

This book focuses on ILC analysis and design with experimental benchmarking. The analysis and design methods coverage include linear and nonlinear dynamics, time-varying, and stochastic dynamics. Both linear and nonlinear designs are considered, where one design for the latter case is for examples where actuation saturation is a possibility.

Key features:

Provides comprehensive coverage of the main approaches to ILC and their relative advantages and disadvantages.
Presents the leading research in the field along with experimental benchmarking results.
Demonstrates how this approach can extend out from engineering to other areas and, in particular, new research into its use in healthcare systems/rehabilitation robotics.

The book is essential reading for researchers and graduate students in iterative learning control, repetitive control and, more generally, control systems theory and its applications.

Table of Contents:
Preface
1 Iterative Learning Control: Origins and General Overview
2 Iterative Learning Control: Experimental Benchmarking
3 An Overview of Analysis and Design for Performance
4 Tuning and Frequency Domain Design of Simple Structure ILC Laws
5 Optimal ILC
6 Robust ILC
7 Repetitive Process-Based ILC Design
8 Constrained ILC Design
9 ILC for Distributed Parameter Systems
10 Nonlinear ILC
11 Newton Method Based ILC
12 Stochastic ILC
13 Some Emerging Topics in Iterative Learning Control
Appendix A
References
Index




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