Sparsity-Constrained Linear Dynamical Systems

This volume provides a comprehensive overview of recent research advances in the upcoming field of sparse control and state estimation of linear dynamical systems. The contents offer a detailed introduction to the subject by combining classical control theory and compressed sensing. It covers conceptual foundations, including the formulation, theory, and algorithms, and outlines numerous remaining research challenges. Specifically, the book provides a detailed discussion on observability, controllability, and stabilizability under sparsity constraints. It also presents efficient, systematic, and rigorous approaches to estimating the sparse initial states and designing sparse control inputs. It also gives background materials from real analysis and probability theory and includes applications in network control, wireless communication, and image processing. It serves as a compendious source for graduate students and researchers in signal processing and control systems to acquire a thorough understanding of the underlying unified themes. The academic and industrial professionals working on the design and optimization of sparsity-constrained systems also benefit from the exposure to the array of recent works on linear dynamical systems and related mathematical machinery.

Introduces the analysis of control-theoretic concepts under sparsity constraints Provides a comprehensive treatment of the subject including background materials from real analysis and probability theory Outlines applications of sparse control in networked control and wireless communication

Auteur

Geethu Joseph received the B. Tech. degree in electronics and communication engineering from the National Institute of Technology, Calicut, India, in 2011, and the M. E. degree in signal processing and the Ph.D. degree in electrical communication engineering (ECE) from the Indian Institute of Science (IISc), Bangalore, in 2014 and 2019, respectively. She was a postdoctoral fellow with the Department of Electrical Engineering and Computer Science at Syracuse University, NY, USA, from 2019 to 2021. She is currently a tenured assistant professor in the signal processing systems group at the Delft University of Technology, Delft, Netherlands. Dr. Joseph was awarded the 2022 IEEE SPS Best PhD dissertation award and the 2020 SPCOM Best Doctoral Dissertation award. She is also a recipient of the Prof. I. S. N. Murthy Medal in 2014 for being the best M. E. (signal processing) student in the ECE dept., IISc, and the Seshagiri Kaikini Medal for the best Ph.D. thesis of the ECE dept. at IISc for the year 2019-'20. Dr. Joseph holds 35+ peer-reviewed publications in the fields of signal processing, communications, and control theory. She is an associate editor of the IEEE Sensors Journal and an active reviewer for major journals and conferences in signal processing, communications, and control theory. Her research interests include statistical signal processing, network control, and machine learning.

Chandra R. Murthy received his B.Tech. degree in Electrical Engineering from the Indian Institute of Technology (IIT) Madras in 1998, and M.S. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University and the University of California, San Diego, in 2000 and 2006, respectively. In 2007, he joined the Department of Electrical Communication Engineering at the Indian Institute of Science (IISc) Bangalore, India, where he is currently working as a Professor. His research interests are in the areas of sparse control of linear dynamical systems, Bayesian algorithms for sparse signal recovery and their performance analysis, and 5G/6G wireless communications. He has over 75 journals and 100 conference papers to his credit. He is a recipient of the MeitY Young Faculty Fellowship from the Government of India and Prof. Satish Dhawan state award for engineering from the Karnataka State Government. He is a fellow of the IEEE and the Indian National Academy of Engineering (INAE).

Contenu

Sparsity in Linear Systems.- Sparse Initial State: Estimation Algorithms.- Sparse Initial State: Theoretical Guarantees.- Sparse Control Inputs: Algorithms.- Sparse Control Inputs: Theoretical Guarantees.