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Fuzzy-Neural Network Adaptive Sliding Mode Tracking Control for Interconnected System

  • Yan-xin Zhang
  • Hai-rong Dong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

Fuzzy neural network adaptive tracking controller is designed to realize the tracking control for a class of unknown nonlinear interconnected systems. No constraint or matching conditions of the uncertain terms are required. For the low dimensions unknown dynamic of the subsystems and the high one of the interconnected terms, two classes of fuzzy rules are adopted respectively to approximate the unknowns. The neural network is used to counteract the extra gains of the controller. The fuzzy sliding mode control is developed to compensate for the exterior disturb and the fuzzy neural network approximation errors. By the Simultaneity, based on Lyapunov method, the parameters of the systems are regulated on line by the adaptive laws. Global asymptotic stability is assured with the tracking errors converging to a neighborhood of zero.

Keywords

Tracking Error Fuzzy Neural Network Fuzzy Logic System Global Asymptotic Stability Gain Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yan-xin Zhang
    • 1
    • 2
  • Hai-rong Dong
    • 1
    • 2
  1. 1.Institute of Automatic control, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044China
  2. 2.The Key Laboratory of Complex Systems and Intelligence Science, Chinese Academy of Sciences, Beijing, 100044China

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