Rogers E., Chu B., Freeman C., Lewin P. Iterative Learning Control Algorithms and Experimental Benchmarking

Wiley, 2023. — 1004 p. — ISBN 978-1-118-53537-0.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 an integrated coverage of the major approaches to-date in terms of basic systems theoretic properties, design algorithms, and experimentally measured performance as well the links with repetitive control and other related areas.Iterative Learning Control: Origins and General Overview
Iterative Learning Control: Experimental Benchmarking
An Overview of Analysis and Design for Performance
Tuning and Frequency Domain Design of Simple Structure ILC Laws
Optimal ILC
Robust ILC
Repetitive Process-Based ILC Design
Constrained ILC Design
ILC for Distributed Parameter Systems
Nonlinear ILC
Newton Method Based ILC
Stochastic ILC
Some Emerging Topics in Iterative Learning Control