Using Recurrent Fuzzy Wavelet Neural Network to Control AC Servo System

A kind of recurrent fuzzy wavelet neural network (RFWNN) is constructed by using recurrent wavelet neural network (RWNN) to realize fuzzy inference. In the network, temporal relations are embedded in the network by adding feedback connections on the first layer of the network, and wavelet basis function is used as fuzzy membership function. An adaptive control scheme based on RFWNN is proposed, in which, two RFWNN are used to identify and control plant respectively. The proposed adaptive control scheme is applied on AC servo control problem, and simulation results are given

[1]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[2]  Jun Wang,et al.  A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  Kwang Y. Lee,et al.  Diagonal recurrent neural networks for dynamic systems control , 1995, IEEE Trans. Neural Networks.

[4]  Bhaskar D. Rao,et al.  On-line learning algorithms for locally recurrent neural networks , 1999, IEEE Trans. Neural Networks.

[5]  Chin-Teng Lin,et al.  A recurrent fuzzy cellular neural network system with automatic structure and template learning , 2004, IEEE Trans. Circuits Syst. I Regul. Pap..

[6]  Mietek A. Brdys,et al.  Dynamic neural controllers for induction motor , 1999, IEEE Trans. Neural Networks.

[7]  Kwang Y. Lee,et al.  An optimal tracking neuro-controller for nonlinear dynamic systems , 1996, IEEE Trans. Neural Networks.

[8]  Chin-Teng Lin,et al.  A Recurrent Fuzzy Coupled Cellular Neural Network System With Automatic Structure and Template Learning , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[9]  Ching-Hung Lee,et al.  Identification and control of dynamic systems using recurrent fuzzy neural networks , 2000, IEEE Trans. Fuzzy Syst..

[10]  T. A. Condarcure,et al.  Recurrent neural-network training by a learning automaton approach for trajectory learning and control system design , 1998, IEEE Trans. Neural Networks.