Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors

The high-performance application of high-power permanent magnet synchronous motors (PMSM) is increasing. PMSM models with accurate parameters are significant for precise control system designs. Acquisition of these parameters during motor operation is a challenging task due to the inherent nonlinearity of motor dynamics. This paper proposes an intelligent model parameter identification method using particle swarm optimization (PSO). PSO, an intelligent computational method based on stochastic search, is shown to be a versatile and efficient tool for this complicated engineering problem. Through both simulation and experiment, this paper verifies the effectiveness of the proposed method in identification of PMSM model parameters. Specifically, stator resistance and load torque disturbance are identified in this PMSM application. Though PMSM is presented, the method is generally applicable to other types of electrical motors, as well as other dynamic systems with nonlinear model structure.