A novel multi‐strategy self‐optimizing SAPSO algorithm for PMSM parameter identification

To address the unsatisfactory performance of particle swarm optimization (PSO), a novel multi-strategy self-optimizing simulated annealing particle swarm optimization (SOSAPSO) method for permanent magnet synchronous motor (PMSM) parameter identification is proposed. The full-rank mathematical model and the fitness function are developed. In SOSAPSO, the velocity term of the PSO is simplified and dynamic opposition-based learning (DOBL) is introduced in the inertia weight update process to avoid population monotonicity. Moreover, A Cauchy-Gaussian hybrid variation strategy based on similarity and density is devised to achieve self-learning in deep regions. Meanwhile, the simulated annealing (SA) with a memory and tempering mechanism is introduced into SOSAPSO, and the greedy optimization algorithm (GOA) is used to enhance local fine-exploitation capabilities when SOSAPSO evolution is stalled. The test results indicate the proposed method can effectively avoid local convergence problems and has better robustness and convergence speed.