Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals (Studies in Systems, Decision and Control, 170, Band 170) - Hardcover

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Hao Shen received the Ph.D. degree in control science and engineering from Nanjing University of Science and Technology, Nanjing, China, in 2011. From February 2013 to March 2014, he was a Post-Doctoral Fellow with the Department of Electrical Engineering, Yeungnam University, Republic of Korea. Since 2011, he has been with Anhui University of Technology, China, where he is currently a full Professor and a Ph.D. Supervisor with the School of Electrical and Information Engineering. Dr. Shen has served on the technical program committee for several international conferences, such as The 2017 Australian and New Zealand Control Conference, ANZCC 2017, The 2018 Australian and New Zealand Control Conference, ANZCC 2017, The 34th Chinese Control Conference, CCC2015. He is an Associate Editor for several international journals, including Journal of The Franklin Institute, Applied Mathematics and Computation, Neural Processing Letters, IEEE Access, Transactions of the Institute Measurement and Control, Discrete Dynamics in Nature and Society and Mathematical Problems in Engineering. He has published 2 Monographs and more than 200 research papers in international referred journals, cited in Google 12501 times and his h-index is 65. He was a recipient of the Highly Cited Researcher Award by Clarivate Analytics (formerly, Thomson Reuters) in 2019-2022.

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This book introduces the principle theories and applications of control and filtering problems to address emerging hot topics in feedback systems. With the development of IT technology at the core of the 4th industrial revolution, dynamic systems are becoming more sophisticated, networked, and advanced to achieve even better performance. However, this evolutionary advance in dynamic systems also leads to unavoidable constraints. In particular, such elements in control systems involve uncertainties, communication/transmission delays, external noise, sensor faults and failures, data packet dropouts, sampling and quantization errors, and switching phenomena, which have serious effects on the system’s stability and performance. This book discusses how to deal with such constraints to guarantee the system’s design objectives, focusing on real-world dynamical systems such as Markovian jump systems, networked control systems, neural networks, and complex networks, which have recently excited considerable attention. It also provides a number of practical examples to show the applicability of the presented methods and techniques.

This book is of interest to graduate students, researchers and professors, as well as R&D engineers involved in control theory and applications looking to analyze dynamical systems with constraints and to synthesize various types of corresponding controllers and filters for optimal performance of feedback systems.