Systems Engineering and Electronics ›› 2019, Vol. 41 ›› Issue (9): 2100-2106.doi: 10.3969/j.issn.1001-506X.2019.09.25

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Multiple model adaptive control of nonlinear systems based on clustering method and neural network

TANG Weiqiang1,2,3, LONG Wenkun1,2, SUN Lijuan1,2, HUANG Xiaoli1,2   

  1. 1. College of Electrical and Information Engineering, Lanzhou University of Technology,
    Lanzhou 730050, China; 2. Key Laboratory of Gansu Advanced Control for Industrial Processes,
    Lanzhou University of Technology, Lanzhou 730050, China; 3. National Experimental Teaching Center of
    Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
  • Online:2019-08-27 Published:2019-08-20

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Abstract:

Aiming at the nonlinear system with parameter jump, a multiple model adaptive control method is proposed, which combines clustering algorithm and neural network. Firstly, the fuzzy c-means clustering algorithm is used to adaptively cluster the input and output data. Then the recursive least squares method is used to establish multiple fixed models for all kinds of data. To improve the transient performance of the system, two adaptive models are established and a robust adaptive controller is designed. In addition, in order to compensate the nonlinear part of the system, the nonlinear prediction model is established and the nonlinear neural network adaptive controller is designed. The proposed method can ensure the stability of the control switching system. Finally, the controller is smoothly switched through the performance index. The simulation results show that the proposed method can guarantee the system has a good control performance.

Key words: nonlinear systems, multiple model method, adaptive control, fuzzy clustering, neural network

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