Parameter identification of permanent magnet synchronous motors using quasi-opposition-based particle swarm optimization and hybrid chaotic particle swarm optimization algorithms

Favourable performance of designed controllers for Permanent magnet synchronous motors (PMSMs), deeply depends on accurate model and parameters of PMSM. This paper proposes two improved versions of particle swarm optimization (PSO) for identification of all-six electrical and mechanical parameters of PMSM. This research inserts two different strategies to overcome premature convergence of PSO. In the first version, the PSO is incorporated with quasi-opposition-based learning (QOBL) to be accelerated and also to diversify its search moves. In the second version of proposed improved PSO, in an attempt to diversify and manifold search moves, a chaotic local search is inserted in the PSO to further enhance its global search ability. Aforementioned algorithms are tested on problem of PMSM parameter identification and also 30 CEC2014 benchmark functions. The obtained results demonstrate that the proposed algorithms in this research beside of good solution quality are very effective and robust so that they produce similar and promising results over repeated runs. Subsequently, a comparison of the proposed algorithm with two recent well performing algorithms, i.e. covariance matrix adaptation-evolution strategy (CMA-ES) and success-history based adaptive differential evolution with linear population size reduction (L-SHADE) confirmed a comparable performance of our proposed algorithm. Statistical analysis of obtained results on CEC2014 functions by Wilcoxon test also indicated that the proposed algorithm has a significant performance over other compared state-of-art algorithms.