Title :
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
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;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561024
