Robust H∞ filtering for Markov jump systems with mode-dependent quantized output and partly unknown transition probabilities - Yao, Deyin; Lu, Renquan; Xu, Yong; Wang, Lijie (Signal processing, 2017)
•This paper considers the H∞ filtering for MJSs subject to mode-dependent quantized output and unknown transition probabilities, which are more practical in the realistic systems. Hence, the obtained results of our paper are more robust.•Compared with existing studies for MJSs, our study considers more complex conditions: (i) the output quantization signal yq(t) will make system extremely complex to achieve the error dynamics. However, in our paper, the quantization error of the output is transformed into a bounded nonlinearity via a model transformation; (ii) the detail knowledge of transition probabilities is tough to obtain. Hence, more general stochastic stability criteria can be derived through the research of our article. This paper addresses the problem of robust H∞ filtering design for uncertain Markov jump systems (MJSs) with mode-dependent quantized output and partly unknown transition probabilities. The data transmission and exchange are completed over a digital communication channel such that the system outputs need to be quantized before transmission. Attention is mainly concentrated on tackling the parameter uncertainty, unknown transition probabilities and output quantization. The parameter uncertainty considered in this paper is assumed to be norm-bounded, and the exact information of transition probabilities matrix is partly unavailable. The quantization error of the output is transformed into a bounded nonlinearity via a model transformation. A mode-dependent filter is designed to ensure that the filtering error system is stochastically stable and has an H∞ noise attenuation performance index. Finally, simulation results are provided to illustrate the validity of the proposed results.