Stochastic Control and Filtering over Constrained Communication Networks presents up-to-date research developments and novel methodologies on stochastic control and filtering for networked systems under constrained communication networks. It provides a framework of optimal controller/filter design, resilient filter design, stability and performance analysis for the systems considered, subject to various kinds of communication constraints, including signal-to-noise constraints, bandwidth constraints, and packet drops. Several techniques are employed to develop the controllers and filters desired, including:

• recursive Riccati equations;

• matrix decomposition;

• optimal estimation theory; and

• mathematical optimization methods.
  Readers will benefit from the book's new concepts, models and methodologies that have practical significance in control engineering and signal processing. Stochastic Control and Filtering over Constrained Communication Networks is a practical research reference for engineers dealing with networked control and filtering problems. It is also of interest to academics and students working in control and communication networks.

Provides cutting-edge research results of modelling, estimation and control of networked systems with constrainted networks Presents motivated simulation examples to reflect the engineering practice and demonstrate the main focus of the developed control/filtering approaches Covers the issues of constrained resources, including communication rate constraints, signal-to-noise constraints and channel fading

Autorentext

Qinyuan Liu received the B.Eng. degree in measurement and control technology and instrumentation from Huazhong University of Science and Technology, Wuhan, China, in 2012, and the Ph.D. degree in control science and engineering from Tsinghua University, Beijing, China, in 2017. He is currently an Assistant Professor in the Department of Computer Science and Technology, Tongji University, Shanghai, China. From Jul. 2015 to Sep. 2016, he was a Researcher Assistant in the Department of Electronic & Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong. From Jan. 2016 to Jan. 2017, he was an international researcher in the Department of Computer Science, Brunel University London, UK. His research interests include networked control systems, multi-agent systems, and distributed filtering. He is an active reviewer for many international journals. Zidong Wang was born in Jiangsu, China, in 1966. He received the B.Sc. degreein mathematics in 1986 from Suzhou University, Suzhou, China, and the M.Sc. degree in applied mathematics in 1990 and the Ph.D. degree in electrical engineering in 1994, both from Nanjing University of Science and Technology, Nanjing, China. He is currently Professor of Dynamical Systems and Computing in the Department of Information Systems and Computing, Brunel University London, U.K. From 1990 to 2002, he held teaching and research appointments in universities in China, Germany and the UK. Prof. Wang's research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published more than 400+ papers in refereed international journals with an h-index of 100. He is a holder of the Alexander von Humboldt Research Fellowship of Germany, the JSPS Research Fellowship of Japan, William Mong Visiting Research Fellowship of Hong Kong. Prof. Wang serves (or has served) as the Editor-in-Chief for Neurocomputing (Elsevier), Editor-in-Chief for Systems Science and Control Engineering (Taylor & Francis), Deputy Editor-in-Chief for International Journal of Systems Science (Taylor & Francis), and an Associate Editor for 12 international journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Neural Networks, IEEE Transactions on Signal Processing, and IEEE Transactions on Systems, Man, and Cybernetics - Part C. He is a Fellow of the IEEE, a Fellow of the Royal Statistical Society and a member of program committee for many international conferences. Xiao He received the B.E. degree in information technology from the Beijing Institute of Technology, Beijing, China, in 2004, and the Ph.D. degree in control science and engineering from Tsinghua University, Beijing, in 2010. Currently, he is a tenure track Associate Professor with the Department of Automation, Tsinghua University. He has authored more than 40 papers in refereed international journals. His research interests include networked filter and control, and networked fault diagnosis and isolation and their application. Dr. He is now a Full Member of Sigma Xi, the Scientific Research Society, a Senior Member of Chinese Association of Automation (CAA).

Klappentext

Stochastic Control and Filtering over Constrained Communication Networks presents up-to-date research developments and novel methodologies on stochastic control and filtering for networked systems under constrained communication networks. It provides a framework of optimal controller/filter design, resilient filter design, stability and performance analysis for the systems considered, subject to various kinds of communication constraints, including signal-to-noise constraints, bandwidth constraints, and packet drops. Several techniques are employed to develop the controllers and filters desired, including: recursive Riccati equations; matrix decomposition; optimal estimation theory; and mathematical optimization methods. Readers will benefit from the book's new concepts, models and methodologies that have practical significance in control engineering and signal processing. Stochastic Control and Filtering over Constrained Communication Networks is a practical research reference for engineers dealing with networked control and filtering problems. It is also of interest to academics and students working in control and communication networks.

Inhalt
Introduction.- Fundamental Limitations of Networked Systems over Fading Channels.- Event-based H Consensus Control of Multi-agent Systems.- Event-Triggered Resilient Filtering with Measurement Quantization.- Event-based Distributed Filtering of Continuous-time Nonlinear Systems.- Event-based Distributed Filtering over Markovian Switching Topologies.- Event-based Recursive Distributed Filtering.- A Resilient Approach to Distributed Recursive Filter Design.- Consensus-based Recursive Distributed Filtering.- On Kalman-Consensus Filtering with Random Link Failures.- Moving-Horizon Estimation with Binary Encoding Schemes.- Conclusion and Further Work.