Proceedings of the Korean Institute of Intelligent Systems Conference (한국지능시스템학회:학술대회논문집)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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TSK Fuzzy Model Based Hybrid Adaptive Control of Nonlinear Systems

비선형 시스템의 TSK 퍼지모델 기반 하이브리드 적응제어

  • Kim, You-Keun (Dept. of Electrical and Electronic Engineering Yonsei University) ;
  • Kim, Jae-Hun (Dept. of Electrical and Electronic Engineering Yonsei University) ;
  • Hyun, Chang-Ho (Dept. of Electrical and Electronic Engineering Yonsei University) ;
  • Kim, Eun-Tai (Dept. of Electrical and Electronic Engineering Yonsei University) ;
  • Park, Mi-Gnon (Dept. of Electrical and Electronic Engineering Yonsei University)
  • Published : 2004.10.01
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Abstract

In this thesis, we present the Takagi-Sugeno-Kang (TSK) fuzzy model based adaptive controller and adaptive identification for a general class of uncertain nonlinear dynamic systems. We use an estimated model for the unknown plant model and use this model for designing the controller. The hybrid adaptive control combined direct and indirect adaptive control based on TSK fuzzy model is constructed. The direct adaptive law can be showed by ignoring the identification errors and fails to achieve parameter convergence. Thus, we propose an TSK fuzzy model based hybrid adaptive (HA) law combined of the tracking error and the model ins error to adjust the parameters. Using a Lyapunov synthesis approach, the proposed hybrid adaptive control is proved. The hybrid adaptive law (HA) is better than the direct adaptive (DA) method without identifying the model ins error in terms of faster and improved tracking and parameter convergence. In order to show the applicability of the proposed method, it is applied to the inverted pendulum system and the performance is verified by some simulation results.

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