Speed control of PMSM by hybrid genetic Artificial Bee Colony Algorithm

Abstract

Swarm Intelligence is the one of the most efficient and emergent techniques for global optimization. Artificial Bee Colony Algorithm (ABCA) is one of the new swarm intelligent population-based meta-heuristic approaches, inspired by foraging behavior of bees for function optimization. To enhance the efficiency of ABCA optimizer this paper proposes a novel hybrid approach involving genetic algorithms (GA) and Artificial Bee colony (ABC) algorithms. The proposed method is used for tuning Proportional Integral (PI) speed controller in a vector-controlled Permanent Magnet Synchronous Motor (PMSM) Drive. In this application our tuning method focuses on minimizing the Integral Time Absolute Error (ITAE) criterion. Simulation results and as well as comparisons with other methods like conventional Gradient descent method, Genetic algorithm, and Artificial Bee Colony methods shows the effectiveness of hybrid approach. Simulations are carried out using Industrial standard MATLAB/SIMULINK.