摘要
针对永磁同步电机(Permanent Magnet Synchronous Motor, PMSM)多参数辨识速度慢、精度低等问题,提出了一种基于改进樽海鞘群算法的参数辨识方法。采用自适应评估移动策略和基于冯诺依曼拓扑结构的邻域最优引领策略两次更新追随者位置,加强个体间信息交流与协作,进而加快了参数辨识收敛速度;采用反向学习策略以一定变异概率对个体位置进行扰动,算法更易跳出局部最优,进而减小了参数误收敛的可能性。仿真实验表明该算法能快速准确地辨识PMSM参数。
Since the multi-parameter identification of permanent magnet synchronous motor （PMSM） has slow speed and low accuracy, a parameter identification method based on the improved salp swarm algorithm was proposed in this paper. The algorithm firstly adopted the self-adaptive evaluation-move strategy and neighborhood optimum guide strategy based Von Neumann topology to update the position of followers twice, which strengthened information cooperation in the population and accelerated the convergence rate of parameter identification. Secondly, the algorithm used the opposition-based learning strategy to perturb the population position with a certain mutation probability, that avoided local optimum and misconvergence of the parameters. The simulation results show that this algorithm can identify PMSM parameter quickly and accurately.
作者
王梦秋
王艳
纪志成
Wang Mengqiu;Wang Yan;Ji Zhicheng(Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Jiangnan University,Wuxi 214122,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第11期4284-4291,4297,共9页
Journal of System Simulation
基金
国家自然科学基金(61572238)
江苏省杰出青年基金(BK20160001)
关键词
樽海鞘群算法
冯诺依曼结构
反向学习
永磁同步电机
多参数辨识
salp swarm algorithm
Von Neumann topology
opposition-based learning
PMSM
multi-parameter identification
