Heterogeneous multi-subswarm particle swarm optimisation for numerical and parameter estimation of PMSM

Abstract

A heterogeneous multi-subswarm coevolution particle swarm optimisation (HMSCPSO) is proposed for numerical optimisation and parameters identification of PMSM. To improve the algorithm's dynamic optimal performance, the HMSCPSO consists of one adaptive subswarm and several basic subswarms. During the iteration, the best individual in basic subswarm and adaptive subswarm are selected as candidate to construct the elite subswarm. Heterogeneous search strategy was adopted in basic subswarm and adaptive subswarm. The migration scheme is employed for the information exchange between subswarms. The adaptive inertia weight strategy can maintain a balance between exploration and exploitation to ensure the algorithm converges to stable point. To accelerate the convergence rate, immune clonal selection operator with wavelet mutation is applied to elite subswarm. The performance of the proposed algorithm is extensively evaluated on suite of numerical optimisation functions. The results demonstrate good performance of the HMSCPSO in solving numerical problems when compared with others recent variants PSO. The performance of HMSCPSO is further evaluated by its application to the parameters identification of PMSM. The experimental results show that the HMSCPSO can simultaneously identify stator resistance, dq axis inductances and the permanent magnet flux accurately.