This paper studies the problem of model reduction for nonhomogeneous Markovian jump systems. The transition probability matrix of the nonhomogeneous Markovian chain has the characteristic of a polytopic structure. An asynchronous reduced-order model is considered, and the asynchronization is modeled by a hidden Markov model with a partially unknown conditional probability matrix. Under this framework, a new sufficient condition is proposed to ensure that the augmented system is stochastically mean-square stable with a specified level of H-infty performance. Finally, a numerical example is provided to show the effectiveness and advantages of the theoretic results obtained.
Bibliographical noteFunding Information:
Manuscript received January 23, 2019; accepted April 20, 2019. Date of publication May 9, 2019; date of current version December 27, 2019. This work was supported in part by the Science Fund for Creative Research Groups under Grant 61621002, in part by the National Nature Science Foundation of China under Grant 61773131 and Grant U1509217, and in part by the Australian Research Council under Grant DP170102644. Recommended by Associate Editor P. Rapisarda. (Corresponding author: Zheng-Guang Wu.) Y. Shen and Z.-G. Wu are with the State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China (e-mail:, email@example.com; firstname.lastname@example.org).
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- hidden Markov model
- model reduction
- nonhomogeneous Markovian chain
- partially unknown conditional probabilities
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering