GA-PSO optimized online ANFIS based speed controller for Brushless DC motor

Abstract

This paper deals with the application of GA-PSO optimized online Adaptive Neuro Fuzzy Inference System (ANFIS) for the speed control of Brushless DC motor. Learning parameters, i.e., Learning Rate (η), forgetting factor (λ) and steepest descent momentum constant (α) of online ANFIS controller is optimized for different speed-torque operating conditions of Brushless DC motor using hybrid GA-PSO algorithm. The overall speed control system is simulated and validated using MATLAB. The performance of the proposed controller is analyzed and compared with offline ANFIS controller and Proportional Integral Derivative (PID) controller. In order to validate the effectiveness of the proposed controller, simulation is performed under constant load conditions, varying load conditions and varying set speed conditions. Also speed tracking response is investigated for different set speed conditions and different loading conditions. In addition, for effective comparison of the controllers, four performance measures such as maximum overshoot, steady state error, integral of absolute error, and integral of time multiplied absolute error are evaluated and tested for the considered controllers. It has been proved that the proposed controller easily overcomes the drawbacks of offline ANFIS controller and Proportional Integral Derivative (PID) controller.