Hybrid Neural Network and Genetic Algorithms for Self-tuning of PI Controller in DSPM Motor Drive System

Abstract

Due to the nonlinear characteristics of Double Salient Permanent Magnet (DSPM) motor, the fixed-gain Proportional Integer (PI) controller can not perform well at all operating conditions. To increase the robustness of PI controllers, we present a self-tuning PI controller for speed control of DSPM motor drive system. The method is systematic and robust to parameter variations. We first treat the model of the DSPM motor drive. A well-trained multi-layer Neural Network (NN) is used to map the nonlinear relationship between the controller coefficients (Kp, Ki) and the control parameters (switching angles and current). Then we apply genetic algorithm to optimize the coefficients of the PI controller. A second NN is used to evaluate the fitness value of each chromosome in programming process of genetic algorithm. A main advantage of our method is that we do not require the accurate model of DSPM motor (which is always difficult to acquire), and the training process of NN can be done off-line through personnel computer, so that the controller can be implemented with a Digital Signal Processor (DSP-TMS320F2407). The experimental results illuminated that the proposed variable PI controller offers faster dynamic response and better adaptability over wider speed range.