Optimized neural network speed control of induction motor using genetic algorithm

For the high performance drives of induction motor, recurrent artificial neural network (RNN) based self tuning speed controller is proposed. 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. Self tuning controller can change gains of the controller according to system conditions. The gains are composed of the weights of RNN. For the on-line estimation of the weights of RNN, extended Kalman filter (EKF) algorithm should be used. In order to design EKF with optimal constants, simple genetic algorithm is proposed. Genetic algorithm can follow the optimal estimation constants without trial and error efforts. The availability of the proposed controller is verified through the MATLAB and Simulink simulation with the comparison of conventional controller. The simulation results show a significant enhancement in shortening development time and improving system performance over a traditional manually tuned EKF estimation algorithm based neural network controller