摘要
为克服模型不精确和存在外部扰动导致卡尔曼滤波精度下降问题,提出一种改进容积卡尔曼滤波内置式永磁电机无位置传感器控制算法。建立了两相静止坐标下永磁电机状态方程,采用高斯过程回归对系统状态和量测进行学习,并替代容积卡尔曼滤波中的系统状态方程和量测方程。该方法保留了容积卡尔曼滤波算法的辨识精度,提高了模型不精确和存在外部扰动时系统的鲁棒性,实验结果表明,相对于对比卡尔曼滤波、扩展卡尔曼滤波和容积卡尔曼滤波算法,改进的容积卡尔曼滤波算法在辨识精度、实时性及鲁棒性上均更优,具有更广的应用前景。
In order to overcome the problems of model inaccuracy and external disturbance led to the decrease of the kalman filtering accuracy,an improved cubature kalman filter is set up in permanent magnet motor position sensor less control algorithm.The state equation of Permanent magnet motor in the two-phase stationary coordinates is established,and gaussian process regression is used to identy the system state and measurement,and alternative cubature kalman filter in the system state equation and measurement equation.The identification accuracy of cubature KF is retained,the system robustness is improved with model inaccuracy and external disturbance.The experimental results show that the improved cubature kalman filtering algorithm identification accuracy,real-time,and robustness are better than kalman filter and extended kalman filtering and cubature kalman filtering algorithm,and it has a wider application prospect.
作者
王迪
WANG Di(Department of Information and Control Engineering,Shenyang Urban Construction University,Shenyang 110167,China)
出处
《控制工程》
CSCD
北大核心
2019年第4期777-782,共6页
Control Engineering of China
基金
2018年度沈阳城市建设学院科研发展基金项目(XKJ2018004)
关键词
改进容积卡尔曼滤波
参数辨识
无位置传感器
精度
Improved cubature kalman filter
parameter inentification
position sensorless
accuracy
