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Adaptive Neural Model Based Fault Tolerant Control for Multi-variable Process

  • Cuimei Bo
  • Jun Li
  • Zhiquan Wang
  • Jinguo Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

A new FTC scheme based on adaptive radial basis function (RBF) neural network (NN) model for unknown multi-variable dynamic systems is proposed. The scheme designs an adaptive RBF model to built process model and uses extended Kalman filter (EKF) technique to online learn the fault dynamics. Then, a model inversion controller is designed to produce the fault tolerant control (FTC) actions. The proposed scheme is applied to a three-tank process to evaluate the performance of the scheme. The simulation results show that component fault can be quickly compensated so that the system performances are recovered well.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cuimei Bo
    • 1
    • 2
  • Jun Li
    • 2
  • Zhiquan Wang
    • 1
  • Jinguo Lin
    • 2
  1. 1.College of Automation Nanjing University of Sciences and, Technology Nanjing, Jiangsu, 210094China
  2. 2.College of Automation Nanjing University of Technology, Nanjing, Jiangsu, 210009China

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