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Robust H fuzzy output-feedback control with multiple probabilistic delays and multiple missing measurements

  • Hongli Dong*
  • , Zidong Wang
  • , Daniel W.C. Ho
  • , Huijun Gao
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Daqing Petroleum Institute
  • Brunel University London
  • City University of Hong Kong

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, the robust H-control problem is investigated for a class of uncertain discrete-time fuzzy systems with both multiple probabilistic delays and multiple missing measurements. A sequence of random variables, all of which are mutually independent but obey the Bernoulli distribution, is introduced to account for the probabilistic communication delays. The measurement-missing phenomenon occurs in a random way. The missing probability for each sensor satisfies a certain probabilistic distribution in the interval [0 1]. Here, the attention is focused on the analysis and design of H fuzzy output-feedback controllers such that the closed-loop Takagi-Sugeno (T-S) fuzzy-control system is exponentially stable in the mean square. The disturbance-rejection attenuation is constrained to a given level by means of the H-performance index. Intensive analysis is carried out to obtain sufficient conditions for the existence of admissible output feedback controllers, which ensures the exponential stability as well as the prescribed H performance. The cone-complementarity- linearization procedure is employed to cast the controller-design problem into a sequential minimization one that is solved by the semi-definite program method. Simulation results are utilized to demonstrate the effectiveness of the proposed design technique in this paper.

Original languageEnglish
Article number5444946
Pages (from-to)712-725
Number of pages14
JournalIEEE Transactions on Fuzzy Systems
Volume18
Issue number4
DOIs
StatePublished - Aug 2010

Keywords

  • Discrete-time fuzzy systems
  • fuzzy control
  • multiple missing measurements
  • multiple probabilistic time delays
  • networked-control systems (NCSs)
  • robust H control
  • stochastic systems

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