Paper Info
Title
Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors
Abstract
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.
Year
DOI
Venue
2008
10.1016/j.engappai.2007.10.002
Eng. Appl. of AI
Keywords
Field
DocType
pmsm model,permanent magnet,synchronous motor,pmsm application,though pmsm,particle swarm,intelligent model parameter identification,nonlinear model structure,intelligent computational method,electrical motor,pmsm model parameter,high-performance application,dynamic system,electric motor,particle swarm optimization
Particle swarm optimization,Mathematical optimization,Nonlinear system,Torque,Simulation,Computer science,Control theory,Stator,Control system,Dynamical system,Electric motor,Permanent magnet synchronous motor
Journal
Volume
Issue
ISSN
21
7
Engineering Applications of Artificial Intelligence
Citations 
PageRank 
References 
30
1.61
6
Authors
3
Name
Order
Citations
PageRank
Li Liu1468.81
Wenxin Liu2968.39
David A. Cartes36411.09