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Volume 5 Issue 2
Mar.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2018  >  5(2): 457-462
Hongjun Yang and Jinkun Liu, "An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 457-462, Mar. 2018. doi: 10.1109/JAS.2017.7510820
Citation: Hongjun Yang and Jinkun Liu, "An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 457-462, Mar. 2018. doi: 10.1109/JAS.2017.7510820
shu

An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems

doi: 10.1109/JAS.2017.7510820
  • 1.

    State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

  • 2.

    School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China

Funds:

the National Natural Science Foundation of China 61703402

the National Natural Science Foundation of China 61374048

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    Abstract
  • This paper focuses on designing an adaptive radial basis function neural network (RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively. The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives (CMD) system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method. Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.

     

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