Optimal Control of Brushless Dc Motor using Soft Computing Technique Particle Swarm Optimisation and Grasshopper

The research paper introduces an optimization approach that combines Grass Hopper Optimization (GOA) and PSO techniques. Determining the optimal PID controller parameters to regulate a BLDC motor's speed is the aim. The Simulink environment in MATLAB software is employed to simulate the BLDC motor. Optimally tuned PID controllers are then employed to select an optimal PI controller for BLDC motor. Lastly, the efficacy of this proposed optimization method is validated through various simulations along with experimental results obtained from the BLDC motor control.