A Comparison Study of Some Metaheuristic Methods for Field Oriented Control Based Induction Motors

Thanks to the accuracy, high reliability and excellent performance, field-oriented control (FOC) based induction motor (IM) drives are used in power applications. Generally, three PI controllers are used for speed control, and decoupling control of torque and flux by d-q components of stator current in FOC based IM. Most of the time, the gains of these PI controllers are tuned by trial and error or Ziegler Nichols method. However, these methods cannot provide a high dynamic response. In this paper, some metaheuristic methods such as Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms are used to determine the optimal values of the PI controller parameters to improve the dynamic performance of FOC based IM. The simulation results show that the best dynamic performance is obtained by GWO algorithm although its convergence speed is slower than the used other algorithms.