Event-Driven State Estimation for Stochastic Networked Systems
- 1st Edition - October 30, 2025
- Latest edition
- Authors: Cong Huang, Peng Mei, Hamid Reza Karimi
- Language: English
Event-Driven State Estimation for Stochastic Networked Systems offers a comprehensive and clear explanation of recent developments in event-based state estimation for stocha… Read more
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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
- Summarizes the latest research concepts, conclusions and applications of event-based state estimation methodologies for stochastic systems under limited communication networks
- Addresses the analysis and design of various types of stochastic systems under event-triggered mechanisms
- Utilizing state estimation strategies, challenges such as recursive state estimation, fusion estimation, and state and fault estimation for different stochastic systems are explored
- Utilizing state estimation strategies, the book challenges such as recursive state estimation, fusion estimation, and state and fault estimation for different stochastic systems are explored
Postgraduate students majoring on control engineering, signal processing, nonlinear systems, system modeling and applied mathematics; researchers; control engineers working on system modeling, robust control and stochastic systems; signal processing engineers working on estimation over communication networks; mathematicians and physicists working on complex systems
1. Introduction
1.1 Overview of networked environment
1.1.1 Censored measurement
1.1.2 Sensor saturation and sensor failure/degradation
1.1.3 Randomly occurring nonlinearties
1.2 Overview of stochastic systems
1.2.1 Uncertain systems
1.2.2 Multi-rate systems
1.2.3 State-saturated systems
1.2.4 Complex networks
1.3 Overview of event-triggered mechanism
1.4 Preview of this book
1.5 Abbreviations and notations References
2. State-saturated resilient filtering for nonlinear complex networks under event-triggering protocols
2.1 Introduction
2.2 Problem formulation
2.3 Main results
2.3.1 The design of SSRF
2.3.2 Boundedness analysis
2.4 Experimental example
2.4.1 Effectiveness of the designed SSRF scheme
2.4.2 Comparison of results between different triggering thresholds
2.4.3 Conventional Kalman filtering versus the proposed SSRF algorithm
2.5 Conclusion References
3. A Dynamically event-triggered approach to recursive filtering with censored measurements and parameter uncertainties
3.1 Introduction
3.2 Problem formulation
3.3 Main results
3.3.1 The design of the filter
3.3.2 Boundedness analysis
3.3 Illustrative examples
3.4 Conclusion References
4. Distributed state-of-charge estimation for lithium-ion batteries with random sensor failure under dynamic event-triggering protocol
4.1 Introduction
4.2 Problem formulation
4.2.1 Preliminaries
4.2.2 Dynamic model of LBs
4.2.3 Measurement model of BTV
4.2.4 Communications in sensor networks
4.3 Main results
4.4 Experimental results
4.4.1 Results for parameter identification
4.4.2 Estimation results under different cases
4.4.3 Algorithm comparison
4.5 Conclusion References
5. Event-based fusion estimation for multi-rate systems subject to sensor degradations
5.1 Introduction
5.2 Problem formulation
5.3 Main results
5.3.1 The design of local filters
5.3.2 Fusion estimation
5.4. Simulation results
5.5 Conclusion References
6. Event-triggering robust fusion estimation for a class of multi-rate systems subject to censored observations
6.1 Introduction
6.2 Problem formulation
6.3 Main results
6.3.1 The design of local filters
6.3.2 Effects of the event-triggering mechanism
6.3.3 Fusion estimation scheme
6.4 Simulation example
6.5 Conclusion References
7. Dynamic event-triggering joint state and unknown input estimation for nonlinear systems with random sensor failure
7.1 Introduction
7.2 Problem formulation
7.3 Main results
7.4 Performance analysis
7.4.1 Boundedness
7.4.2 Monotonicity
7.5 Illustrative examples
7.6 Conclusion References
8. State and fault estimation for nonlinear systems subject to censored measurements: a dynamic event-triggered case
8.1 Introduction
8.2 Problem formulation
8.3 Main results
8.3.1 The design of the estimator
8.3.2 Boundedness analysis
8.4 Simulation results
8.5 Conclusion References
9. Event-triggering state and fault estimation for a class of nonlinear systems subject to sensor saturations
9.1 Introduction
9.2 Problem formulation
9.3 Main results
9.4 Experimental simulation
9.5 Conclusion References Outlook Index
1.1 Overview of networked environment
1.1.1 Censored measurement
1.1.2 Sensor saturation and sensor failure/degradation
1.1.3 Randomly occurring nonlinearties
1.2 Overview of stochastic systems
1.2.1 Uncertain systems
1.2.2 Multi-rate systems
1.2.3 State-saturated systems
1.2.4 Complex networks
1.3 Overview of event-triggered mechanism
1.4 Preview of this book
1.5 Abbreviations and notations References
2. State-saturated resilient filtering for nonlinear complex networks under event-triggering protocols
2.1 Introduction
2.2 Problem formulation
2.3 Main results
2.3.1 The design of SSRF
2.3.2 Boundedness analysis
2.4 Experimental example
2.4.1 Effectiveness of the designed SSRF scheme
2.4.2 Comparison of results between different triggering thresholds
2.4.3 Conventional Kalman filtering versus the proposed SSRF algorithm
2.5 Conclusion References
3. A Dynamically event-triggered approach to recursive filtering with censored measurements and parameter uncertainties
3.1 Introduction
3.2 Problem formulation
3.3 Main results
3.3.1 The design of the filter
3.3.2 Boundedness analysis
3.3 Illustrative examples
3.4 Conclusion References
4. Distributed state-of-charge estimation for lithium-ion batteries with random sensor failure under dynamic event-triggering protocol
4.1 Introduction
4.2 Problem formulation
4.2.1 Preliminaries
4.2.2 Dynamic model of LBs
4.2.3 Measurement model of BTV
4.2.4 Communications in sensor networks
4.3 Main results
4.4 Experimental results
4.4.1 Results for parameter identification
4.4.2 Estimation results under different cases
4.4.3 Algorithm comparison
4.5 Conclusion References
5. Event-based fusion estimation for multi-rate systems subject to sensor degradations
5.1 Introduction
5.2 Problem formulation
5.3 Main results
5.3.1 The design of local filters
5.3.2 Fusion estimation
5.4. Simulation results
5.5 Conclusion References
6. Event-triggering robust fusion estimation for a class of multi-rate systems subject to censored observations
6.1 Introduction
6.2 Problem formulation
6.3 Main results
6.3.1 The design of local filters
6.3.2 Effects of the event-triggering mechanism
6.3.3 Fusion estimation scheme
6.4 Simulation example
6.5 Conclusion References
7. Dynamic event-triggering joint state and unknown input estimation for nonlinear systems with random sensor failure
7.1 Introduction
7.2 Problem formulation
7.3 Main results
7.4 Performance analysis
7.4.1 Boundedness
7.4.2 Monotonicity
7.5 Illustrative examples
7.6 Conclusion References
8. State and fault estimation for nonlinear systems subject to censored measurements: a dynamic event-triggered case
8.1 Introduction
8.2 Problem formulation
8.3 Main results
8.3.1 The design of the estimator
8.3.2 Boundedness analysis
8.4 Simulation results
8.5 Conclusion References
9. Event-triggering state and fault estimation for a class of nonlinear systems subject to sensor saturations
9.1 Introduction
9.2 Problem formulation
9.3 Main results
9.4 Experimental simulation
9.5 Conclusion References Outlook Index
- Edition: 1
- Latest edition
- Published: October 30, 2025
- Language: English
CH
Cong Huang
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
Affiliations and expertise
Nantong University, ChinaPM
Peng Mei
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
Affiliations and expertise
Politecnico di Milano, ItalyHK
Hamid Reza Karimi
Dr. Karimi received the B.Sc. (First Hons.) degree in power systems from the Sharif University of Technology, Tehran, Iran, in 1998, and the M.Sc. and Ph.D. (First Hons.) degrees in control systems engineering from the University of Tehran, Tehran, in 2001 and 2005, respectively. His research interests are in the areas of control systems/theory, mechatronics, networked control systems, intelligent control systems, signal processing, vibration control, ground vehicles, structural control, wind turbine control and cutting processes. He is an Editorial Board Member for some international journals and several Technical Committee. Prof. Karimi has been presented a number of national and international awards, including Alexander-von-Humboldt Research Fellowship Award (in Germany), JSPS Research Award (in Japan), DAAD Research Award (in Germany), August-Wilhelm-Scheer Award (in Germany) and been invited as visiting professor at a number of universities in Germany, France, Italy, Poland, Spain, China, Korea, Japan, India.
Affiliations and expertise
Professor of Applied Mechanics, Department of Mechanical Engineering, Politecnico di Milano, Milan, ItalyRead Event-Driven State Estimation for Stochastic Networked Systems on ScienceDirect