03816nam a22006015i 4500 978-981-97-7090-8 DE-He213 20251030114613.0 cr nn 008mamaa 241211s2024 si | s |||| 0|eng d 9789819770908 978-981-97-7090-8 10.1007/978-981-97-7090-8 doi TJ212-225 TJ210.2-211.495 TJFM bicssc TEC007000 bisacsh TJFM thema Joseph, Geethu. author. aut http://id.loc.gov/vocabulary/relators/aut 88795 Sparsity-Constrained Linear Dynamical Systems [electronic resource] : From Compressed Sensing to Control Theory / by Geethu Joseph, Chandra R. Murthy. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XVI, 98 p. 3 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Tracts in Electrical and Electronics Engineering, 2731-4219 Sparsity in Linear Systems -- Sparse Initial State: Estimation Algorithms -- Sparse Initial State: Theoretical Guarantees -- Sparse Control Inputs: Algorithms -- Sparse Control Inputs: Theoretical Guarantees. 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. . Control engineering. Robotics. 59148 Automation. 59149 Computational intelligence. 64052 Dynamical systems. Control, Robotics, Automation. Computational Intelligence. 64052 Dynamical Systems. Control and Systems Theory. 65868 Murthy, Chandra R. author. aut http://id.loc.gov/vocabulary/relators/aut 88796 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819770892 Printed edition: 9789819770915 Printed edition: 9789819770922 Springer Tracts in Electrical and Electronics Engineering, 2731-4219 66106 https://doi.org/10.1007/978-981-97-7090-8 ZDB-2-INR ZDB-2-SXIT 65633 65633