基于容积卡尔曼滤波PMSM无位置传感器控制
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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.
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.
引文
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[13]荀瑞新.基于容积卡尔曼滤波的外弹道数据处理[J].自动化技术与应用,2017, 36(3):15-17.Xun R X. External Trajectory Data Processing Based on Cubature Kalman Filter[J]. Techniques of Automation and Applications, 2017,36(3):15-17.
[14]马彪~1,贺建军~(1,2),李厚杰~1,等.基于拉普拉斯方法的大规模高斯过程分类算法[J].控制与决策,2017, 32(7):1319-1324.Ma B~1,He J J~(1,2),Li H J1, et al. Large-scale Gaussian process classification via Laplace's method[J]. Control and Decision, 2017,32(7):1319-1324.
[15]张云鹏,李利平~*,贺,鹏,秦承,等.隧道围岩大变形高斯过程回归预测模型及其工程应用[J].科学技术与工程,2018, 18(1):122-125.Zhang Y P, Li L P~*, He Peng, et al. Gaussian process regression prediction model for the big deformation of the tunnel rock and its ap-plication[J]. Science Technology and Engineering, 2018, 18(1):122-125.
[16]罗亦泳.基于高斯过程的大坝变形预测模型[J].浙江工业大学学报,2016,44(5):543-546.Luo Y Y. A Gaussian process based model for predicting deformations of dams[J]. Journal of Zhejiang University of Technology, 2016, 44(5):543-546.
[2]尚喆,赵荣祥,窦汝振,等.基于自适应滑模观测器的永磁同步电机无位置传感器控制研究[J].中国电机工程学报,2007, 27(3):23-27.Shang Z, Zhao R X, Dou R Z, et al. Research on sensorless control method of PMSM based on an adaptive sliding mode observer[J].Proceedings of the CSEE, 2007, 27(3):23-27.
[3]朱其新,张正,杨辉,等.基于无传感器的PMSM电流控制策略的研究[J].控制工程,2014,21(4):547-554.Zhu Q X, Zhang Z, Yang H, et al. Research on Sensorless PMSM Current Control Strategies[J]. Control Engineering of China, 2014,21(4):547-554.
[4]石鼎.基于滑模观测器的PMSM无位置传感器控制[J].控制工程,2017, 24(2):414-422.Shi D. Sensorless Control of PMSM Based on Sliding Mode Observer[J]. Control Engineering of China, 2017, 24(2):414-422.
[5]陈思溢.永磁同步电动机无位置传感器全分数阶滑模控制系统研究[D].南京:南京航空航天大学,2016.
[6]蒋佳晨,黎英,闫亚妮,等.基于变结构理论的步进电机无传感器控制[J].控制工程,2016, 23(1):18-23.Jing J C, Li Y, Yan Y N, et al. T Sensorless Control of Stepper Motor Based on Variable Structure Theory[J]. Control Engineering of China, 2016, 23(1):18-23.
[7]肖烨然,刘刚,宋欣达,等.基于改进滑模观测器的永磁同步电机无位置传感器I/F起动方法[J].电力自动化设备,2015, 35(8):95-102.Xiao Y Y, Liu G, Song X D, et al. Sensorless I/F startup based on modified sliding mode observer for PMSM[J]. Electric Powcr Automation Equipment, 2015, 35(8):95-102.
[8]李旭春,张鹏严,乐阳,等.具有参数辨识的永磁同步电机无位置传感器控制[J].电工技术学报,2016,31(14):139-145.Li X C, Zhang P Y, Le Y, et al. Sensorless Control of Permanent Magnet Synchronous Motor with Online Parameter Identification[J].Transactions of China Electrotechnical society, 2016, 31(14):139-145.
[9]丁家琳.容积卡尔曼滤波算法研究及其在电机状态估计中的应用[D].成都:西南交通大学,2011.
[10] Guo Q D, Luo R F, Wang L M, et al. A Shaft Sensorless Control for PMSM Using Direct Neural Network Adaptive Observer[J].IECON'96, 1996, 13(2):1729-1734.
[11] Luo R F, Wang L M, Guo Q D, et al. Direct Neural Network Adaptive Observer Control for PMSMr[J]. ICIPS'97, 1997, 10(5):414-418.
[12]许振伟,蒋静坪,骆再飞,等.模糊模型算法控制的永磁同步电动机位置伺服系统.电工技术学报,2003, 18(4):99-102.Xu Z W, Jiang J P, Luo Z F, et al. Permanent Magnet Synchronous Motor Position Servo System Controlled by Fuzzy Model Algorithmic Control[J]. Transactions of China Electrotechnical society,2003, 18(4):99-102.
[13]荀瑞新.基于容积卡尔曼滤波的外弹道数据处理[J].自动化技术与应用,2017, 36(3):15-17.Xun R X. External Trajectory Data Processing Based on Cubature Kalman Filter[J]. Techniques of Automation and Applications, 2017,36(3):15-17.
[14]马彪~1,贺建军~(1,2),李厚杰~1,等.基于拉普拉斯方法的大规模高斯过程分类算法[J].控制与决策,2017, 32(7):1319-1324.Ma B~1,He J J~(1,2),Li H J1, et al. Large-scale Gaussian process classification via Laplace's method[J]. Control and Decision, 2017,32(7):1319-1324.
[15]张云鹏,李利平~*,贺,鹏,秦承,等.隧道围岩大变形高斯过程回归预测模型及其工程应用[J].科学技术与工程,2018, 18(1):122-125.Zhang Y P, Li L P~*, He Peng, et al. Gaussian process regression prediction model for the big deformation of the tunnel rock and its ap-plication[J]. Science Technology and Engineering, 2018, 18(1):122-125.
[16]罗亦泳.基于高斯过程的大坝变形预测模型[J].浙江工业大学学报,2016,44(5):543-546.Luo Y Y. A Gaussian process based model for predicting deformations of dams[J]. Journal of Zhejiang University of Technology, 2016, 44(5):543-546.