Hybrid Particle Swarm Optimization and Gravitational Search Algorithm for BLDC Motor Speed Control

This paper presents a new approach of a hybrid optimization technique between particle swarm optimization and gravitational search algorithm (PSO-GSA), which is used to achieve the optimum parameters of a PID controller by optimizing two parameters (K
_p
, K
_i
) and setting (K
_d
) as a constant value . This PID controller is employed to control the speed of the (BLDCM) brushless DC motor by forcing the rotor to follow a preselected speed track. Three different objective functions are utilized with the GSA technique and compared to select the one that achieves the best BLDC motor rendering results. The selected objective function is also used with (PSO),(PSO-GSA) optimization techniques and the three optimization procedures (PSO), (GSA), and (PSO-GSA) are compared with each other, The result of this comparison shows that the combined (PSO-GSA) optimization method gives better performance characteristics, reduced overshoot and lowest steady state error compared to other optimization techniques. The hybrid (PSO-GSA) also gives better performance in speed tracking, reduced torque ripples and smooth speed variation which are important considerations to the (BLDCM).