GA-Based Adaptive Fuzzy-Neural Control for a Class of MIMO Systems

  • Yih-Guang Leu
  • Chin-Ming Hong
  • Hong-Jian Zhon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4491)


A GA-based adaptive fuzzy-neural controller for a class of multi-input multi-output nonlinear systems, such as robotic systems, is developed for using observers to estimate time derivatives of the system outputs. The weighting parameters of the fuzzy-neural controller are tuned on-line via a genetic algorithm (GA). For the purpose of on-line tuning the weighting parameters of the fuzzy-neural controller, a Lyapunov-based fitness function of the GA is obtained. Besides, stability of the closed-loop system is proven by using strictly-positive-real (SPR) Lyapunov theory. The proposed overall scheme guarantees that all signals involved are bounded and the outputs of the closed-loop system track the desired output trajectories. Finally, simulation results are provided to demonstrate robustness and applicability of the proposed method.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yih-Guang Leu
    • 1
  • Chin-Ming Hong
    • 1
  • Hong-Jian Zhon
    • 1
  1. 1.Department of Industrial Education, National Taiwan Normal University, 162, Ho-Oing E. Road, Sec 1, TaipeiTaiwan

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