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

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, 100084, China

    Min Wang & Donghua Zhou

Authors
  1. Min Wang
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  2. Donghua Zhou
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Wang, M., Zhou, D. (2005). Output Based Fault Tolerant Control of Nonlinear Systems Using RBF Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_12

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  • DOI: https://doi.org/10.1007/11427469_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25914-5

  • Online ISBN: 978-3-540-32069-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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