Nonlinear System Adaptive Control by Using Multiple Neural Network Models

  • Xiao-Li Li
  • Yun-Feng Kang
  • Wei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


Multiple radial based function (RBF)neural network models are used to cover the uncertainty of time variant nonlinear system, and multiple element controllers are set up based on the multiple RBF models. At every sample time, the closest model is selected by an index function which is formed by the integration of model output error. The element controller based on this model will be switched as the controller of the controlled system. This kind of multiple model adaptive controller (MMAC)is an extension of the MMAC in linear system, and it can improve the transient response and performance of the controlled system greatly.


Nonlinear System Radial Base Function Adaptive Control Radial Base Function Neural Network Discrete Time System 
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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  1. 1.
    Narendra, K.S., Balakrishnan, J.: Adaptive Control Using Multiple Models. IEEE Trans. Automatic Control 42(2), 171–187 (1997)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Narendra, K.S., Xiang, C.: Adaptive Control of Discrete-time Systems Using Multiple Models. IEEE Trans. Automatic Control 45(9), 1669–1685 (2000)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Li, X.L., Wang, W.: Minimum Variance Based Multi-model Adaptive Control. In: Proc. IFAC World Congress, Beijing, China, pp. 325–329 (1999)Google Scholar
  4. 4.
    Li, X.L., Wang, W., Wang, S.N.: Multiple Model Adaptive Control for Discrete Time Systems. In: American Control Conference, Arlington, Virginia, USA, pp. 4820–4825 (2001)Google Scholar
  5. 5.
    Chen, L.J., Narendra, K.S.: Nonlinear Adaptive Control Using Neural Networks and Multiple Models. Automatica 37(8), 1245–1255 (2001)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Narendra, K.S., Driollet, O.: Stochastic Adaptive Control Using Multiple Estimation Models. Int. J. Adapt. Control Signal Process 15(3), 287–317 (2001)MATHCrossRefGoogle Scholar
  7. 7.
    Li, X.L., Zhang, W.C., Wang, L.: Stochastic Adaptive Control Using Multiple Models for Improved Performance in the Presence of Bounded Disturbance, Impulsive Dynamical Systems and Applications. In: DCDIS Proceedings, Wuxi China, vol. 3, pp. 1061–1068 (2005)Google Scholar
  8. 8.
    Zufiria, P.J., et al.: Neural Adaptive Control of Non-linear Plants via a Multiple Inverse Models Approach. Int. J. Adapt. Control Signal Process 13(4), 219–239 (1999)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiao-Li Li
    • 1
  • Yun-Feng Kang
    • 1
  • Wei Wang
    • 2
  1. 1.Department of Automation, Information and Engineering SchoolUniversity of Science and Technology BeijingBeijingP.R.China
  2. 2.Research Center of Information and ControlDalian University of TechnologyDalianP.R. China

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