Fundamentals of Stochastic Signals, Systems and Estimation Theory 3rd Edition | 15.82 MB
Title: Fundamentals of Stochastic Signals, Systems and Estimation Theory
Author: Branko Kovačević · Željko Đurović · Zoran Banjac
Category: Nonfiction, Science & Nature, Technology, Electricity, Mathematics, Applied, Engineering
Language: English | 707 Pages | ISBN: 9783031942396
Description:
This edited book presents recent advances in state estimation, robust control synthesis, system identification, fault detection, localization, and optimization, with a particular emphasis on interval-based methods and set-membership techniques. Covering both theoretical developments and practical applications, the book brings together contributions from recognized experts in these research areas.
Topics include set-based state estimation in varied dynamical system settings, sliding-mode predictive and state-feedback control, innovative optimization algorithms, zonotopic fault detection and identification, as well as distributed moving horizon estimation. The proposed methods are illustrated through practical simulation studies in robotics, autonomous vehicles, fuel cell systems, and sensor networks.
Intended for researchers, engineers, and graduate students in control systems, applied mathematics, and various engineering disciplines, this book offers both a rigorous foundation and cutting-edge approaches for addressing uncertainty in complex dynamical systems.
This edited book presents recent advances in state estimation, robust control synthesis, system identification, fault detection, localization, and optimization, with a particular emphasis on interval-based methods and set-membership techniques. Covering both theoretical developments and practical applications, the book brings together contributions from recognized experts in these research areas.
Topics include set-based state estimation in varied dynamical system settings, sliding-mode predictive and state-feedback control, innovative optimization algorithms, zonotopic fault detection and identification, as well as distributed moving horizon estimation. The proposed methods are illustrated through practical simulation studies in robotics, autonomous vehicles, fuel cell systems, and sensor networks.
Intended for researchers, engineers, and graduate students in control systems, applied mathematics, and various engineering disciplines, this book offers both a rigorous foundation and cutting-edge approaches for addressing uncertainty in complex dynamical systems.
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