Event-Driven State Estimation for Stochastic Networked Systems offers a comprehensive and clear explanation of recent developments in event-based state estimation for stochastic systems within limited communication networks, bringing together existing and emerging concepts. It provides a series of the latest results in, including but not limited to, recursive state estimation, fusion estimation, and state and fault estimation. The book provides practitioner readers with practical tools for the analysis and design of stochastic systems under limited communication networks, capturing recent advances in theory, technological aspects and real-world applications of advanced event-based state estimation methodologies. Realistic research problems are addressed in each chapter, with numerical and simulation results provided to reflect engineering practice, while demonstrating the main focus of the developed estimation approaches. The book is an advanced-level resource presented in an accessible manner, appealing to senior students as a core reference and to researchers and practitioners alike
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Dr Cong Huang received his Ph.D. degree in control science and engineering from Donghua University, China. From 2019 to 2020, he was a joint PhD student at the Politecnico di Milano, Italy. He is currently an Associate Professor with the School of Transportation, Nantong University, China. He has published around 30 papers in refereed international journals. His research interests include stochastic control and filtering, vehicle platoon control, and estimation for vehicle-borne lithium-ion batteries
Dr Peng Mei currently holds a joint postdoctoral position at the Politecnico di Milano and the University of Genoa. He earned his PhD in New Energy Vehicle Engineering from Beihang University in January 2024. From 2019 to 2021, he served as a Visiting Scholar in the Department of Mechanical Engineering at Politecnico di Milano in Milan, Italy. He has published around 20 papers in refereed international journals. His research focuses on various aspects of vehicle control strategies, reinforcement learning, and battery modelling
Event-Driven State Estimation for Stochastic Networked Systems offers a comprehensive and clear explanation of recent developments in event-based state estimation for stochastic systems within limited communication networks, bringing together existing and emerging concepts. It provides a series of the latest results in, including but not limited to, recursive state estimation, fusion estimation, and state and fault estimation. The book provides practitioner readers with practical tools for the analysis and design of stochastic systems under limited communication networks, capturing recent advances in theory, technological aspects and real-world applications of advanced event-based state estimation methodologies. Realistic research problems are addressed in each chapter, with numerical and simulation results provided to reflect engineering practice, while demonstrating the main focus of the developed estimation approaches. The book is an advanced-level resource presented in an accessible manner, appealing to senior students as a core reference and to researchers and practitioners alike
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