摘要
提出用蚁群矢量移动算法同时辨识永磁同步电动机(PMSM)伺服系统负载运行时的转动惯量和负载转矩,以便速度环的PI参数整定和转矩补偿。其原理是把蚁群的平面矢量移动正交分解成水平和垂直两个方向,分别和转动惯量及负载转矩对应,每只蚂蚁的位置对应一种转动惯量和负载转矩组合的可能解。运用采样得到的d轴电流和速度序列数据,基于最小方差原理,建立蚂蚁信息素散发模型,使得蚂蚁位置与实际转动惯量和负载转矩越接近,蚂蚁散发的信息素越大。根据蚁群总信息素分布情况,计算蚁群的理想分布期望,与实际蚁群分布比较后,启发蚁群矢量移动,并朝最优方向聚集,收敛点为辨识最优解。精心选择蚁群的规格化分布区间,把动态的蚁群分布区间转化成规格化区间,改善收敛速度。仿真和实验表明,能同时辨识转动惯量和负载转矩,误差小;蚁群规模变大,误差更小,调整后的伺服系统动态性能变好。
An ant colony vector moving algorithm is proposed to identify the load torque and moment of inertia for load-running permanent magnet synchronous motor（PMSM） servo system while run-time loading,in order to adjust the PI parameters and compensate the torque.Vector moving is decomposed into horizontal and vertical directions,one for moment of inertia,the other for load torque,and every ant position means one possible solution.With the sampled sequences d-axis current and speed data,based on the minimum variance principle,the pheromone expression model is established,making the closer distance between the ant position and actual load and inertia,the more pheromone.The expecting distribution for ant colony is calculated after the total pheromone statistics.The ant colony is inspired to move towards the optimal direction with the convergence point for the identified results.The normalized distribution for ant colony is selected to improve convergence performance when the dynamic ant colony distribution is converted to the normalized range.Simulations and experiments show the two parameters can be identified at the same time with the small error;and the error can become smaller if ant colony scale becomes bigger,and the dynamic performance is excellent after adjusting PI according to the identified values.
出处
《电工技术学报》
EI
CSCD
北大核心
2011年第6期18-25,共8页
Transactions of China Electrotechnical Society
基金
国家自然科学基金资助项目(50877030)
