A Novel Parameter Estimation Method for PMSM by Using Chaotic Particle Swarm Optimization With Dynamic Self-Optimization

In this study, a novel parameter estimation method for permanent magnet synchronous motor (PMSM) of chaotic particle swarm optimization with dynamic self-optimization (DSCPSO) is proposed, where the voltage source inverter (VSI) nonlinearity is estimated simultaneously with the parameters to achieve real-time compensation of VSI nonlinearity. In DSCPSO, the tent chaos theory is introduced into the updating of particle swarm algorithm (PSO) populations, inertia weights and learning factors to enhance its ability to explore potentially better regions. Moreover, a memory tempering annealing (MTA) strategy is employed to guarantee particle pluralistic learning, which combines the superior robustness of the simulated annealing algorithm (SA) while enhancing population diversity. Furthermore, to achieve a reasonable tradeoff between exploration and exploitation, a dynamic lens imaging opposition-based learning (DLIOBL) and domain optimization strategy based on evolutionary information is designed, i.e., DLIOBL in the pre-evolutionary stage guarantees the depth of the exploration learning, while the domain optimization strategy is performed in the post-evolutionary stage accelerates the exploitation operation and avoids the problem of slow convergence in the late stages of PSO. The proposed method is applied to the parameter estimation of PMSM and the experimental results show that, the proposed method can track the VSI nonlinearity and variable parameter better than the conventional method under different working conditions.