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Disturbance attenuation of nonlinear control systems using an observer-based fuzzy feedback linearization control

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  • Chen, Chung-Cheng
  • Hsu, Chao-Hsing
  • Chen, Ying-Jen
  • Lin, Yen-Feng

Abstract

The almost disturbance decoupling and trajectory tracking of nonlinear control systems using an observer-based fuzzy feedback linearization control (FLC) is developed. Because not all of the state variables of the nonlinear dynamic equations are available, a nonlinear state observer is employed to estimate the state variables. The feedback linearization control guarantees the almost disturbance decoupling performance and the uniform ultimate bounded stability of the tracking error system. Once the tracking errors are driven to touch the global final attractor with the desired radius, the fuzzy logic control is immediately applied via human expert’s knowledge to improve the convergence rate. One example, which cannot be solved by the first paper on the almost disturbance decoupling problem, is proposed in this paper to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by our proposed approach. In order to demonstrate the practical applicability, the study has investigated a pendulum control system.

Suggested Citation

  • Chen, Chung-Cheng & Hsu, Chao-Hsing & Chen, Ying-Jen & Lin, Yen-Feng, 2007. "Disturbance attenuation of nonlinear control systems using an observer-based fuzzy feedback linearization control," Chaos, Solitons & Fractals, Elsevier, vol. 33(3), pages 885-900.
    • Handle: RePEc:eee:chsofr:v:33:y:2007:i:3:p:885-900
    DOI: 10.1016/j.chaos.2006.01.110
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    1. Yan, Jun-Juh & Shyu, Kuo-Kai & Lin, Jui-Sheng, 2005. "Adaptive variable structure control for uncertain chaotic systems containing dead-zone nonlinearity," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 347-355.
    2. Kim, Jae-Hun & Park, Chang-Woo & Kim, Euntai & Park, Mignon, 2005. "Adaptive synchronization of T–S fuzzy chaotic systems with unknown parameters," Chaos, Solitons & Fractals, Elsevier, vol. 24(5), pages 1353-1361.
    3. Zhang, Hongbin & Yu, Juebang, 2005. "LMI-based stability analysis of fuzzy large-scale systems with time delays," Chaos, Solitons & Fractals, Elsevier, vol. 25(5), pages 1193-1207.
    4. Huang, Xia & Cao, Jinde & Huang, De-Shuang, 2005. "LMI-based approach for delay-dependent exponential stability analysis of BAM neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 24(3), pages 885-898.
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    Cited by:

    1. Chen, Chung-Cheng & Chien, Ting-Li & Chen, Ying-Chen & Lin, Wen-Jiun & Yang, Shu-Hao, 2009. "A new simple unifying approach of finding the state equation model and its practical application," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2464-2472.

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