Constrained state estimation for stochastic jump systems: moving horizon approach

ABSTRACT

We discuss the state estimation advantages for a class of linear discrete-time stochastic jump systems, in which a Markov process governs the operation mode, and the state variables and disturbances are subject to inequality constraints. The horizon estimation approach addressed the constrained state estimation problem, and the Bayesian network technique solved the stochastic jump problem. The moving horizon state estimator designed in this paper can produce the constrained state estimates with a lower error covariance than under the unconstrained counterpart. This new estimation method is used in the design of the restricted state estimator for two practical applications.

KEYWORDS:

• Filter
• stochastic jump systems
• state estimation
• moving horizon estimation
• constraints

Acknowledgments

The authors gratefully acknowledge the reviewers and the associate editor for their valuable comments, which have improved the quality of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported partially by the China Scholarship Council [201406790017]; the 111 Project [B12018]; and the National Natural Science Foundation of China [61573112], [U1509217].

Notes on contributors

Qing Sun

Qing Sun received the Bachelor’s degree from the Department of Automation, Jiangnan University, Wuxi, China, in 2011. Currently, she is a Ph.D. Candidate in the Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Institute of Automation. From 2014 to 2016, she was a visiting student with the School of Electrical and Electronic Engineering, University of Adelaide, SA,  Australia.  Her research interest covers state estimation theory, advanced control theory, and stochastic signal processing, etc.

Cheng-Chew Lim

Cheng-Chew Lim received his B.Sc. degree (with honours) in Electronic and Electrical Engineering, and his Ph.D. degree from Loughborough University, Leicestershire, U.K. He is Reader and Associate Professor in Electrical & Electronic Engineering, and Head of School of Electrical and Electronic Engineering, the University of Adelaide, Australia. His research interests are in the areas of systems and control, wireless communications and optimization techniques and applications. He is serving as an editorial board member for theJournal of Industrial and Management Optimization, and has served as guest editor of a number of journals, including Discrete and Continuous Dynamical System-Series B, and the Chair of the IEEE Chapter on Control and Aerospace Electronic Systems at the IEEE South Australia Section.

Fei Liu

Fei Liu received his B.Sc. degree in electrical technology and M.Sc. degree in industrial automation from Wuxi Institute of Light Industry, Wuxi, China, in 1987 and 1990, respectively, and the Ph.D. degree in control science and control engineering from Zhejiang University, Hangzhou, China, in 2002. From 1990 to 1999, he was an Assistant, a Lecturer, and an Associate Professor with Wuxi Institute of Light Industry. Since 2003, he has been a Professor with the Institute of Automation, Jiangnan University, Wuxi. From 2005 to 2006, he was a Visiting Professor with the University of Manchester, Manchester, U.K. His research interests include advanced control theory and applications, batch process control engineering, statistical monitoring and diagnosis in industrial processes, and intelligent technique with emphasis on fuzzy and neural systems.