双重遗忘卡尔曼滤波PMLSM无位置传感控制研究  

PMLSM without Position Sensing Control of Double Forgetting Kalman Filter

作　　者：朱军[1] 李香君[1] 付融冰 吴宇航[1] 田淼[1] 

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454000

出　　处：《系统仿真学报》2018年第2期672-678,共7页Journal of System Simulation

基　　金：河南省教育厅科学技术重点研究项目(12A470004);河南省高校基本科研业务费专项资金(NSFRF140115)

摘　　要：针对EKF估计PMLSM位置存在模型不精确、噪声统计特性不确定时估计精度不高,且有可能导致滤波发散的问题,提出一种双重遗忘卡尔曼滤波法,它是在EKF的基础上引入自适应渐消因子,实现第一重遗忘,引入Sage-Husa自适应滤波法,实现第二重遗忘。实验表明:该方法不论是同步速度还是负载突变,均按正弦规律递减,负载突变前、后速度稳定误差最大值分别为0.469%、0.943%,最终将稳定在0.167%附近,跟踪效果随仿真时间的加长而更好。UsingextendedKalmanfilter （EKF） to estimate the position of permanent magnet linear synchronous motor（PMLSM）, the model is not accurate, the noise properties areuncertain,and may lead to the problem of filtering divergence,4double forgetting Kalman filter （DFKF） method was proposed. Adaptive fading factor on the basisof EKF was introduced to achieve the first forgetting, andthe Sage-Husa adaptive filter algorithm was introduced to realize the second forgetting. The experiments show that DFKF diminishesaccording to the law of sineregardless synchronous speed change or load mutation;the stable error is 0.469% or 0.943% before or after the load mutation; the final error stabilizes near 0.167%;the effects will be better with the longer time of the simulation.

关 键 词：PMLSM 卡尔曼滤波 自适应渐消因子 Sage-Husa自适应滤波 双重遗忘卡尔曼滤波 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]