Return to author index

Volume: 4 Issue: 2

Permanent Magnet Synchronous Motor Parameter Identification using Particle Swarm Optimization

Li Liu, Pramesh Chand, Richard Chbeir, Lisa Mathew, K. S. Easwarakumar, Sunaina Premkumar, Uma Lakshmanan, Suprema Raj, Susan Elias, S. J. Ovaska, X. Z. Gao, X. Wang, Wenxin Liu, David A. Cartes, Ly Fie Sugianto

Abstract:
High performance application of 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 operations is a challenging task due to the inherent nonlinearity of motor dynamics. This paper proposes an intelligent model parameter identification method using the particle swarm optimization (PSO) approach. As an intelligent computational method based on stochastic search, PSO 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 discussed, the method is generally applicable to other types of electrical motors, as well as to other dynamic systems with nonlinear model structure.

Keywords:
Parameter identification, Particle Swarm Optimization, permanent magnet synchronous motors.

doi:10.5019/j.ijcir.2004.139

Full Text PDF
















^ TOP