Model Reduction of Markovian Jump Systems with Uncertain Probabilities

Ying Shen, Zheng Guang Wu, Peng Shi, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


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.

Original languageEnglish
Article number8710340
Pages (from-to)382-388
Number of pages7
JournalIEEE Transactions on Automatic Control
Issue number1
Publication statusPublished - 2020 Jan


  • Asynchronization
  • 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


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