Fuzzy Control and Filtering for Nonlinear Singularly Perturbed Markov Jump Systems

Yueying Wang, Choon Ki Ahn, Huaicheng Yan, Shaorong Xie

Research output: Contribution to journalArticlepeer-review

72 Citations (Scopus)


This article addresses the control and filtering problems for Markov jump singularly perturbed systems approximated by Takagi-Sugeno fuzzy models. The underlying transition probabilities (TPs) are assumed to vary randomly in a finite set, which is characterized by a higher level TP matrix. The mode-and variation-dependent fuzzy static output-feedback controller (SOFC) and filter are designed, respectively, to fulfill the control and filtering purposes. To facilitate the fuzzy SOFC synthesis, the closed-loop system is transformed into a fuzzy piecewise-homogeneous Markov jump singularly perturbed descriptor system (MJSPDS) by descriptor representation. A rigorous proof of mean-square exponential admissibility for the resulting fuzzy MJSPDS is presented. The criterion ensuring the mean-square exponential stability of the fuzzy filtering error system is further formed based on similar procedures. By setting the specific forms of the related matrix variables, the solutions for the predesigned fuzzy SOFC and filter are furnished, respectively. Finally, feasibility and validities of the developed fuzzy control and filtering results are verified by two practical examples.

Original languageEnglish
Article number9146349
Pages (from-to)297-308
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number1
Publication statusPublished - 2021 Jan


  • Filtering
  • Takagi-Sugeno fuzzy model
  • output-feedback control
  • piecewise-homogeneous Markov jump system
  • singularly perturbed system (SPS)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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