Event-based state and unknown input estimation for uncertain systems with stochastic nonlinearities

ABSTRACT

In this paper, the event-based state and unknown input estimation (SUIE) problem is investigated for a class of stochastic systems subject to parameter uncertainties and stochastic nonlinearities. For the purpose of reducing the energy consumption in data transmission, an event-triggering protocol is employed to regulate whether the current measurement is transmitted by the sensor. Utilising the event-triggered measurement, a recursive estimator is constructed to concurrently estimate the state and the unknown input. The upper bounds of estimation error covariances are given explicitly for both the state and the unknown input estimates. By means of the completing-the-square technique and Lagrange multiplier method, the estimator gain matrices are designed which minimise the obtained upper bounds. Finally, a numerical example is given to show the effectiveness of the proposed SUIE method.

Keywords:

• Unknown input
• uncertain systems
• stochastic systems
• event-triggering mechanism
• state estimation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grants numbers 62073071 and 61703093], the Fundamental Research Funds for the Central Universities, the Startup Foundation for Introducing Talent of Anhui Polytechnic University [grant number 2020YQQ063].

Notes on contributors

Sijing Zhang

Sijing Zhang received his B.Sc. degree in mathematics and applied mathematics from Donghua University, Shanghai, China, in 2013. He is currently pursuing the Ph.D. degree with the School of Information Science and Technology, Donghua University, Shanghai, China. From 2016 to 2017, he was a visiting Ph.D. student with the Institute for automatic control and complex systems (AKS), University of Duisburg-Essen, Germany. His current research interests include network communication, stochastic control and filtering. Mr. Zhang is a very active reviewer for many international journals.

Hailong Tan

Hailong Tan received his B.Sc. degree in statistics from Donghua University, Shanghai, China, in 2014. He is currently pursuing the Ph.D. degree with the School of Information Science and Technology, Donghua University, Shanghai, China. From 2017 to 2018, he was a visiting Ph.D. student with the Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, U.K. His current research interests include network communication, robust filtering, and sampled-data systems. Mr. Tan is a very active reviewer for many international journals.

Huisheng Shu

Huisheng Shu received his B.Sc. degree in Mathematics in 1984 from Anhui Normal University, Wuhu, China, and M.Sc. degree in Applied Mathematics in 1990 and Ph.D. degree in Control Theory in 2005, both from Donghua University, Shanghai, China. He is currently a Professor at Donghua University, Shanghai, China. He has published 20 papers in refereed international journals. His research interests include the mathematical theory of stochastic systems, robust control and robust filtering.

Nan Li

Nan Li received her B. E. degree in Automation from Wuhan University of Hydraulic and Electrical Engineering, Yichang, China, in 2003 and the Master degree in Control Theory and Control Engineering from Zhejiang University of Technology, Hangzhou, China, in 2009. She is currently a lecturer with the College of Information Science and Technology, Donghua University, Shanghai, China. From 2003 to 2012, she was a lecturer with the College of Mechanical and Electrical Engineering, Zhejiang Ocean University, Zhoushan, China. Her research interests include neural networks, complex networks, nonlinear systems and bioinformatics.