Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates

  • Authors:

  • Sung Jin Yoo;Jin Bae Park;Yoon Ho Choi

  • Affiliations:

  • Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea;Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea;School of Electronic Engineering, Kyonggi University, Suwon, Kyonggi-Do 443-760, Republic of Korea

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2007

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Abstract

This paper proposes an indirect adaptive control method using self recurrent wavelet neural networks (SRWNNs) for dynamic systems. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). However, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN can store the past information of wavelets. In the proposed control architecture, two SRWNNs are used as both an identifier and a controller. The SRWNN identifier approximates dynamic systems and provides the SRWNN controller with information about the system sensitivity. The gradient-descent method using adaptive learning rates (ALRs) is applied to train all weights of the SRWNN. The ALRs are derived from discrete Lyapunov stability theorem, which are applied to guarantee the convergence of the proposed control system. Finally, we perform some simulations to verify the effectiveness of the proposed control scheme.