Quantized feedback control for Markov jump linear systems with incomplete transition probabilities

Author

Zhiqiang Zuo ; Chang Liu ; Yijing Wang

Author_Institution

Tianjin Key Lab. of Process Meas. & Control, Tianjin Univ., Tianjin, China

fYear

2013

fDate

25-27 May 2013

Firstpage

762

Lastpage

766

Abstract

This paper considers the quantized feedback control for Markov jump linear systems with incomplete transition probabilities where the effect of current mode observation logarithmic quantizer is counted for. A sufficient condition is proposed in the framework of linear matrix inequalities. Therefore, the coarsest quantization density can be obtained using an optimization problem with convex constraints. A numerical example illustrates the effectiveness of proposed scheme.

Keywords

convex programming; distributed parameter systems; feedback; linear matrix inequalities; linear systems; networked control systems; probability; quantisation (signal); stochastic systems; Markov jump linear systems; coarsest quantization density; convex constraints; current mode observation logarithmic quantizer; incomplete transition probabilities; linear matrix inequalities; networked control systems; optimization problem; quantized feedback control; Feedback control; Linear systems; Markov processes; Quantization (signal); Stability analysis; State feedback; Symmetric matrices; Incomplete transition probabilities; Markov jump systems; Networked control sys-tems; Quantized feedback;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2013 25th Chinese

Conference_Location

Guiyang

Print_ISBN

978-1-4673-5533-9

Type

conf

DOI

10.1109/CCDC.2013.6561024

Filename

6561024