Open AccessPMSM parameter identification based on improved PSO
Author(s) -
Zongwei Li,
Dongdong Chen,
Ying Chen,
Hongdan Lei,
Huimin Zhu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1754/1/012235
Subject(s) - control theory (sociology) , gaussian , particle swarm optimization , identification (biology) , computer science , mathematics , algorithm , artificial intelligence , physics , botany , control (management) , quantum mechanics , biology
The results of the standard PSO algorithm are easy to fluctuate and the time-varying error is too large. By introducing the strategy of Gaussian decline and Gaussian disturbance, an improved PSO motor parameter identification method is proposed. When the motor parameters change, the improved PSO method can be used to identify the motor parameters faster, more accurate and more stable. The simulation results show that the improved PSO overcomes the recognition results of the standard PSO and improves its recognition accuracy.