JJAP Conference Proceedings

JJAP Conf. Proc. 4, 011612 (2016) doi:10.7567/JJAPCP.4.011612

Intelligent neural network design for nonlinear control using simultaneous perturbation stochastic approximation (SPSA) optimization

Adrienn Dineva1,2, Annamária R. Várkonyi-Kóczy3,4, József K. Tar5, Vincenzo Piuri6

  1. 1Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Budapest, Hungary
  2. 2Doctoral School of Computer Science, Universita’ degli Studi di Milano, Crema, Italy
  3. 3Institute of Mechatronics & Vehicle Engineering, Óbuda University, Budapest Hungary
  4. 4Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia
  5. 5Institute of Applied Mathematics, Óbuda University, Budapest, Hungary
  6. 6Department of Computer Science, Universita’ degli Studi di Milano, Crema, Italy
  • Received September 27, 2015
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Recently intelligent control systems using neural networks (NN) have been widely applied. NNs are used to approximate complicated mathematical functions of nonlinear systems. This paper considers the design of an intelligent NN controller for nonlinear systems where the neural network is trained with the simultaneous perturbation stochastic approximation (SPSA) algorithm instead of the classical training methods. The main contribution of the SPSA method that it requires only two objective function measurements per iteration regardless of the dimension of the optimization problem. The effectiveness of the proposed scheme is demonstrated by the adaptive control of the translational oscillator/rotational actuator (TORA) system. Results of numerical simulation substantiate that the suggested approach leads to a fast way of controller designs by providing acceptable performance.

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