Optimal design of adaptive PI controller for SRM system using neural network and genetic algorithms

Due to the nonlinear characteristics of Switched Reluctance Motor (SRM), the fixed-gain Proportional Integer (PI) controller can not perform well at all operating conditions. To increase the robustness of PI controllers, the authors present an adaptive PI controller for speed control of SRM drive system under Angular Position Control (APC). Firstly, a well-trained multi-layer Neural Network (NN) is used to model the nonlinear relationship between the controller coefficients (Kp, Ki) and the control parameters (switching angles and current). Then an improved genetic algorithm is used to optimize the coefficients of the PI controller, while the fitness value of each chromosome in genetic programming process is calculated using a NN-based model. Finally, the controller was 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.