Quantised output-feedback design for networked control systems using semi-Markov model approach

ABSTRACT

This paper focuses on the stability analysis and controller design for a family of discrete-time linear systems with quantised signals in both control input channel and measurement output channel. Quantisation density of each quantizer is considered to be variable, and the variation is governed by a semi-Markov chain. The quantisation density can be adjusted to reduce the amount of transmitted data if the requirement of system performance allows. By constructing a quantisation-error-dependent Lyapunov function, sufficient conditions for σ-error mean-square stability of the considered system are given. With the aid of cone complementarity linearisation algorithm, a set of mode-dependent output-feedback controllers is designed to achieve the required σ-error mean-square stability of the closed-loop system. Finally, a numerical example is provided to illustrate the validity of the proposed control method.

Keywords:

• Networked control systems
• quantised signals
• semi-Markov chain
• σ-error mean-square stability
• variable quantisation density

Disclosure statement

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

Additional information

Notes on contributors

Weiyang Sun

Weiyang Sun received the B.Sc. degree in measurement, control technology and instrument and M. Sc. degree in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2017 and 2019, respectively. She is currently pursuing the Ph.D. degree with the Group of Networked Sensing and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China. Her research interest is networked control systems.

Zepeng Ning

Zepeng Ning received the B.S. degree in electronic information science and technology and the M.S. degree in control science and engineering both from the Harbin Institute of Technology, Harbin, China, in 2014 and 2016, respectively. He is currently working toward the Ph.D. degree in control science and engineering in Harbin Institution of Technology. His research interests include stochastic switching systems and model predictive control.