PMSM Driver Based on Hybrid Particle Swarm Optimization and CMAC

被引：0
|
作者：
Tu, Ji [1 ]
Liu, Heping [1 ]
Cao, Shaozhong [2 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Informat Engn, Beijing, Peoples R China
[2] Beijing Inst Graph Commun, Sch Informat & Mech Engn, Beijing, Peoples R China
来源：
2010 INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT (CCCM2010), VOL III | 2010年
基金：
北京市自然科学基金;
关键词：
Particle Swarm Optimization; CMAC; PMSM;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A novel hybrid particle swarm optimization (PSO) and cerebellar model articulation controller (CMAC) is introduced to the permanent magnet synchronous motor (PMSM) driver. PSO can simulate the random learning among the individuals of population and CMAC can simulate the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments and comparisons have been done in MATLAB/SIMULINK. Analysis among PSO, hybrid PSO-CMAC and CMAC feed-forward control is also given. The results prove that the electric torque ripple and torque disturbance of the PMSM driver can be reduced by using the hybrid PSO-CMAC algorithm.
引用
收藏
页码：120 / 123
页数：4
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