A novel hybrid ABF-PSO algorithm based tuning of optimal FOPI speed controller for PMSM drive

Abstract

Bacterial Foraging Optimization algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of optimization process. In the classical BFOA proposed by Passino, during the process of chemotaxis, optimization depends on a random search direction which may lead to delay in reaching global solution. To accelerate the convergence speed of group of bacteria near global optima the chemotactic step has been made adaptive and the resultant is Adaptive Bacterial Foraging Optimization (ABFO). In order to overcome the delay in optimization and to further enhance the performance of ABFO, this paper proposed a new hybrid algorithm combining the features of Adaptive Bacterial Foraging (ABF) and Particle Swarm Optimization (PSO) for tuning a Fractional order Proportional Integral speed controller in a vector controlled Permanent Magnet Synchronous Motor Drive. Our tuning method focuses on minimizing the Integral Time Absolute Error (ITAE) criterion. Computer simulations illustrate the effectiveness of the proposed approach compared to that of classical methods and state of art optimization techniques like PSO and ABFO.