Parameter Optimization of Adaptive Flux-Weakening Strategy for Permanent-Magnet Synchronous Motor Drives Based on Particle Swarm Algorithm

Operating in the high-speed range is necessary for high-performance permanent-magnet synchronous motor (PMSM) drives. However, due to the back electromotive force effect, the PMSM is approaching the voltage limit at field decreasing scope. This paper presents a new flux-weakening scheme along with an improved vector control strategy to alleviate the influence of this problem. Control parameters of the anti-windup proportional and integral (AWPI) controller are optimized off-line in relying on an adaptive velocity particle swarm optimization (AVPSO) algorithm. The AVPSO algorithm considers the summation of AWPI measurement error which is the objective function of the optimization problem without knowing the transfer function of the plant. Hence, the tuned flux-weakening controller with a filter is used to set the flux level without saturating the current controllers. Meanwhile, the other controllers of inner and outer loops award a great dynamic and steady-state performance for the PMSM. In the proposed scheme, the flux-weakening control is not dependent on machine parameters that adapts the flux level automatically and provide a fast transition between the constant torque region and the field-weakening region. Effectiveness and advantages of the proposed scheme are presented in this paper through both simulation and experimental results.