基于综合学习粒子群算法的PMSM滑模观测器设计  

Design of Sliding Mode Observer for PMSM Based on Improved Comprehensive Learning Particle Swarm Optimization

作　　者：韩方阵 李国勇[1] 胡春红[2] 

机构地区：[1]太原理工大学,太原030600 [2]金堆城钼业集团有限公司,渭南712101

出　　处：《微特电机》2017年第7期11-13,22,共4页Small & Special Electrical Machines

基　　金：国家自然科学基金项目(51075291)

摘　　要：设计了一种基于改进综合学习粒子群算法优化的PMSM观测器。在静止两相参考系中建立PMSM的数学模型,用改进粒子群算法优化的超螺旋算法观测电机反电动势值,并采用软件锁相环结构估算电机的转速及转子角位置。实验结果表明,所提策略能准确地估计出转子转速及磁通角,且能够在抱闸释放瞬间输出理想的电磁转矩。High precision vector control of permanent magnet synchronous motor drive required accurate motor speed and rotor position, but it was difficult to overcome the measurement error caused by the parameter perturbation and chatte- ring. The sliding mode observer for PMSM by using super twisting algorithm was proposed. Corresponding mathematical model of PMSM in stationary two-phase coordinate was established, based on above model, ICLPSO was formulated to esti- mate back electromotive force of PMSM and software phase lock loop was designed to estimate the rotor speed. Experimental results show that the proposed strategy has good estimating information in rotor speed and flux angle. Moreover, ideal elec- tromagnetic torque generated by the motor can quickly be generated.

关 键 词：超螺旋滑模观测器 改进综合学习粒子群算法 永磁同步电机 软件锁相环 

分 类 号：TM341[电气工程—电机] TM351