Output Based Fault Tolerant Control of Nonlinear Systems Using RBF Neural Networks

  • Min Wang
  • Donghua Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)


In this paper, an output based fault tolerant controller using radius basis function (RBF) neural networks is proposed which eliminates the assumption that all the states are measured given in Polycarpou’s method. Inputs of the neural network are estimated states instead of measured states. Outputs of the neural network compensate the effect of a fault. The closed-loop stability of the scheme is established. An engine model is simulated in the end to verify the efficiency of the scheme.


Nonlinear System Nonlinear Control System Output Feedback Control SISO System High Gain Observer 
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 2005

Authors and Affiliations

  • Min Wang
    • 1
  • Donghua Zhou
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina

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