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Multiple Models Neural Network Decoupling Controller for a Nonlinear System

  • Xin Wang
  • Shaoyuan Li
  • Zhongjie Wang
  • Heng Yue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3174)

Abstract

For a discrete-time nonlinear MIMO system, a multiple models neural network decoupling controller is designed in this paper. At each equilibrium point, the system is expanded into a linear and nonlinear term. These two terms are identified using two neural networkss, which compose one system model. Then, all models, which are got at all equilibrium points, compose the multiple models set. At each instant, the best model is chosen as the system model according to the switching index. To design the controller accordingly, the nonlinear term and the interactions of the best model is viewed as measurable disturbance and eliminated by the use of the feedforward strategy. The simulation example shows that the better system response can be got even when the system is changed around these equilibrium points.

Keywords

Equilibrium Point Nonlinear Term Induction Motor Multiple Model Polynomial Matrice 
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 2004

Authors and Affiliations

  • Xin Wang
    • 1
  • Shaoyuan Li
    • 1
  • Zhongjie Wang
    • 2
  • Heng Yue
    • 3
  1. 1.Institute of AutomationShanghai Jiao Tong University
  2. 2.Depart. of Control Science & EngineeringTongji University
  3. 3.Research Center of AutomationNortheastern University

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