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Linearization of T-S Fuzzy Systems and Robust Optimal Control

  • Kim, Min-Chan (Department of Electrical Engineering, Changwon National University) ;
  • Wang, Fa-Guang (Changwon National University) ;
  • Park, Seung-Kyu (Department of Electrical Engineering, Changwon National University) ;
  • Kwak, Gun-Pyong (Department of Electrical Engineering, Changwon National University) ;
  • Yoon, Tae-Sung (Department of Electrical Engineering, Changwon National University) ;
  • Ahn, Ho-Kyun (Department of Electrical Engineering, Changwon National University)
  • Received : 2010.11.02
  • Accepted : 2010.11.30
  • Published : 2010.12.31

Abstract

This paper proposes a novel linearization method for Takagi.sugeno (TS) fuzzy model. A T-S fuzzy controller consists of linear controllers based on local linear models and the local linear controllers cannot be designed independently because of overall stability conditions which are usually conservative. To use linear control theories easily for T-S fuzzy system, the linearization of T-S fuzzy model is required. However, The linearization of T-S fuzzy model is difficult to be achieved by using existing linearization methods because fuzzy rules and membership functions are included in T-S fuzzy models. So, a new linearization method is proposed for the T-S fuzzy system based on the idea of T-S fuzzy state transformation. For the T-S fuzzy system linearized with uncertainties, a robust optimal controller with the robustness of sliding model control(SMC) is designed.

Keywords

T-S fuzzy control;linearization;robust optimal;sliding mode control

Acknowledgement

Supported by : Changwon National University

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