LAUSR

The research topics related to learning control include adaptive control, neural-network control, and reinforcement learning, to name a few. Iterative learning control (ILC) is also a category of learning control, and it executes the same control task repeatedly during a finite time horizon. Such a control task widely exists in real world, such as machines in the mining industry, robots in an assembly line, and even a basketball player practicing shots. The book summarizes his rich research on ILC from the paradigm of optimization. The author is with the Department of Automatic Control and Systems Engineering at the University of Sheffield, United Kingdom. He has long-term academic research and practice with ILC and introduces the optimization paradigm to the ILC community in the form of “norm-optimal iterative learning control (NOILC).” The first four chapters introduce the concepts and mathematical background knowledge for ILC. Chapter 5 gives a formal formulation for the ILC problem. Chapters 6 and 7 present two straightforward approaches to study ILC; one is the inverse model algorithm, and the other is the gradient algorithm. Chapter 8 introduces research about combining the inverse and gradient algorithms together. The next five chapters are the key content of this monograph, where the NOILC approach is extensively discussed. Finally, Chapter 14 introduces parameter-optimal iterative control, in which the control is parameterized. This book presents a comprehensive study of ILC from the optimization paradigm, more specifically, the NOILC optimization paradigm. The organization is clear, and the necessary fundamentals are self-contained. The mathematical analysis is rigorous, and the algorithms are detail complete. This book is suitable for academic researchers rather than engineering practitioners. It can be used a textbook for graduate students who are interested in ILC. It also gives rich motivations for researchers to study ILC from the optimization perspective. New research topics may spark following the idea of viewing ILC as an optimization problem rather than a control problem.