This book establishes a unified framework for dealing with typical engineering complications arising in modern, complex, large-scale networks such as parameter uncertainties, missing measurement and cyber-attack. Distributed Filtering, Control and Synchronization is a timely reflection on methods designed to handle a series of control and signal-processing issues in modern industrial engineering practice in areas like power grids and environmental monitoring.
It exploits the latest techniques to handle the emerging mathematical and computational challenges arising from, among other things, the dynamic topologies of distributed systems and in the context of sensor networks and multi-agent systems. These techniques include recursive linear matrix inequalities, local-performance and stochastic analyses and techniques based on matrix theory.
Readers interested in the theory and application of control and signal processing will find much to interest them in the new models and methods presented in this book. Academic researchers can find ideas for developing their own research, graduate and advanced undergraduate students will be made aware of the state of the art, and practicing engineers will find methods for addressing practical difficulties besetting modern networked systems
Tabela de Conteúdo
Introduction.- Distributed H-infinity Filtering for Discrete-Time Piecewise Linear Systems.- Consensus Filtering with Stochastic Nonlinearities and Multiple Missing Measurements.- Distributed Filtering for Random Parameter System with Event-Triggering Protocols.- Scalable Consensus Filtering for Uncertain Systems with Round-Robin Protocol.- Partial-Nodes-Based Scalable H-infinity-Consensus Filtering with Censored Measurements.- Distributed H-infinity-Consensus Filtering over Sensor Networks under Deception Attacks.- Distributed Resilient Filtering for Time-Delayed Systems with Stochastic Perturbations.- Finite-Horizon H-infinity-Consensus Control for Multi-Agent Systems with Random Parameters.- Bounded H-infinity Synchronization and H-infinity Filtering for Discrete-Time Complex Networks.
Sobre o autor
Fei Han received the B.Sc. degree in mathematics and applied mathematics from the China University of Mining and Technology, Xuzhou, China, in 2003, the M.Sc. degree in applied mathematics from Henan Normal University, Xinxiang, China, in 2009, and the Ph.D. degree in system analysis and integration from the University of Shanghai for Science and Technology, Shanghai, China, in 2017. In 2018, he was a Senior Research Assistant with the Department of Electronic Engineering, City University of Hong Kong, Hong Kong, for three months.
He is currently an Associate Professor with the Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China. His current research interests include distributed control and filtering. He has published over 40 peer-review papers.
Zidong Wang was born in Jiangsu, China, in 1966. He received the B.Sc. degree in 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 Computer Science, 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 600 papers in international journals. 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 International Journal of Systems Science, the Editor-in-Chief for Neurocomputing, the Editor-in-Chief for Systems Science & Control Engineering, 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 Member of the Academia Europaea, a Member of the European Academy of Sciences and Arts, an Academician of the International Academy for Systems and Cybernetic Sciences, a Fellow of the IEEE, a Fellow of the Royal Statistical Society and a member of program committee for many international conferences.
Hongli Dong received the Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China, in 2012. From 2009 to 2010, she was a Research Assistant with the Department of Applied Mathematics, City University of Hong Kong, Hong Kong. From 2010 to 2011, she was a Research Assistant with the Departmentof Mechanical Engineering, University of Hong Kong, Hong Kong. From 2011 to 2012, she was a Visiting Scholar with the Department of Information Systems and Computing, Brunel University London, Uxbridge, U.K. From 2012 to 2014, she was an Alexander von Humboldt Research Fellow with the University of Duisburg–Essen, Duisburg, Germany.
She is currently a Professor with Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China, where she is also the Director of the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control. Her current research interests include robust control and networked control systems. She is an active reviewer for many international journals. She has published two books and over 80 peer-review papers, including 24 highly cited papers. Also, she had 4 journal editorials. She was awarded the First Prize of Natural Science of Heilongjiang province, the Young Female Scientist of Chinese Association of Automation and etc. She was included into the Highly Cited Researchers 2016 Thomson Reuters
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