The ebook edition of this title is Open Access and freely available to read online.
The method of sparsity has been attracting a lot of attention in the fields related not only to signal processing, machine learning, and statistics, but also systems and control. The method is known as compressed sensing, compressive sampling, sparse representation, or sparse modeling. More recently, the sparsity method has been applied to systems and control to design resource-aware control systems. This book gives a comprehensive guide to sparsity methods for systems and control, from standard sparsity methods in finite-dimensional vector spaces (Part I) to optimal control methods in infinite-dimensional function spaces (Part II).
The primary objective of this book is to show how to use sparsity methods for several engineering problems. For this, the author provides MATLAB programs by which the reader can try sparsity methods for themselves. Readers will obtain a deep understanding of sparsity methods by running these MATLAB programs.
Sparsity Methods for Systems and Control is suitable for graduate level university courses, though it should also be comprehendible to undergraduate students who have a basic knowledge of linear algebra and elementary calculus. Also, especially part II of the book should appeal to professional researchers and engineers who are interested in applying sparsity methods to systems and control.
Table of Contents:
Chapter 1. Introduction
Part I. Sparse Representation for Vectors
Chapter 2. What is Sparsity?
Chapter 3. Curve Fitting and Sparse Optimization
Chapter 4. Algorithms for Convex Optimization
Chapter 5. Greedy Algorithms
Chapter 6. Applications of Sparse Representation
Part II. Sparsity Methods in Optimal Control
Chapter 7. Dynamical Systems and Optimal Control
Chapter 8. Maximum Hands-off Control
Chapter 9. Numerical Optimization by Time Discretization
Chapter 10. Advanced Topics
About the Author :
Dr. Masaaki Nagahara is currently a full professor at Institute of Environmental Science and Technology, University of Kitakyushu, Japan. He is also a visiting professor at Indian Institute of Technology (IIT) Bombay, India, from 2017. His research interests include optimal control, cyber-physical systems, artificial intelligence, signal processing, and machine learning. He received Transition to Practice Award from IEEE Control Systems Society in 2012. He is a Senior Member of IEEE.
Review :
The writing style is live and easy to follow. In particular, I like very much the examples, problems, and Matlab simulations with actual code segments that are included in the first chapters. The proposal is timely and the topic of sparsity in control is very broad. In addition, to the systems and control community, I expect it will appeal to the machine learning community.
