Self tuning neural network controller for induction motor drives

In this paper, recurrent artificial neural network (RNN) based self tuning speed controller is proposed for the high performance drives of induction motor. RNN provides a nonlinear modeling of motor drive system and could give the information of the load variation, system noise and parameter variation of induction motor to the controller through the on-line estimated weights of corresponding RNN. Thus, proposed self tuning controller can change gains of the controller according to system conditions. The gains are composed with the weights of R-NN. For the on-line estimation of the weights of RNN, extended Kalman filter (EKF) algorithm is used. Self tuning controller that is adequate for the speed control of induction motor is designed. The availability of the proposed controller is verified through the MATLAB simulation with the comparison of conventional PI controller.

[1]  K S Narendra,et al.  IDENTIFICATION AND CONTROL OF DYNAMIC SYSTEMS USING NEURAL NETWORKS , 1990 .

[2]  M. A. Hoque,et al.  Artificial neural network based permanent magnet DC motor drives , 1995, IAS '95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting.

[3]  Bimal K. Bose,et al.  Power Electronics and Ac Drives , 1986 .

[4]  Mohamed A. El-Sharkawi,et al.  Development and implementation of self-tuning tracking controller for DC motors , 1990 .

[5]  Lee A. Feldkamp,et al.  Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.

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

[7]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[8]  Bimal K. Bose,et al.  A stator flux oriented vector-controlled induction motor drive with space vector PWM and flux vector synthesis by neural networks , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).