An Effective Sustainable Control of Brushless DC Motor using Firefly Algorithm – Artificial Neural Network based FOPID Controller

This paper proposes a Fractional purchase PID (FOPID) controller based on Firefly Algorithm-Artificial Neural Network (FA-ANN) to effectively control both the speed and torque of Brushless DC Motor (BLDCM). (BLDCM). The traditional PID controller produces sluggish response and is not efficient. In order to overcome the demerits associated with typical controller, an intelligent FOPID controller is proposed. A Modified Firefly Algorithm (FA) is implemented to attain ideal gain parameters of the proposed controller. To enhance MFF performance, the randomized parameters are updated by ANN. The proposed controller is simulated in Matlab/Simulink platform. The functional analysis of this proposed controller is demonstrated and compared with the existent schemes namely genetic algorithm GA-ANN and FA techniques. The experimental prototype with the intended technique is designed at the same time, and the results of the experiments are verified.