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Compressed sensing, also known as sparse representation or sparse modeling, has experienced substantial growth in research fields such as signal processing, machine learning, and statistics. In recent years, this powerful tool has been successfully applied to the design of control systems.
This book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control. This book is intended for graduate students and researchers who already have a foundational understanding of basic calculus and linear algebra. Its primary objective is to equip readers with the practical skills to apply compressed sensing techniques to a range of engineering problems, with a particular emphasis on systems and control. It presents a comprehensive collection of efficient algorithms for addressing the problems discussed in the text. Moreover, the book includes accompanying Python programs, which enable readers to actively experiment with these algorithms first-hand. By engaging with these practical examples, readers will develop a deeper understanding of compressed sensing techniques and their applications to systems and control.
This book is the second edition of the author's previous work, Sparsity Methods for Systems and Control, published by Now Publishers in 2020. This edition incorporates significant updates to reflect the latest advancements in the field. Notably, it includes new chapters and sections covering the following key topics: Distributed optimization, Sparse system identification, Sparse controller design, and Distributed hands-off control.
