UKF估计器对IPMSM 无传感器控制的比较研究

doi:1004-7018(2019)04-0056-05

摘要/Abstract

摘要：

传统的无传感器技术在起动或低速瞬态工况下表现不理想,需要一些新的无传感器方法,而无需任何附加的起动算法。研究了一种新型的内置式永磁同步电动机(IPMSM)无传感器状态估计器。阐述了无迹卡尔曼滤波器(UKF)算法,并对其适应IPMSM d-q坐标系进行了说明。为了评估驱动性能,与基于滑模观测器的估计方法进行了比较。仿真结果表明,UKF能够更精确地估计转子位置,在低速范围内具有较好的性能,但转子的初始位置仍然是保证电机起动成功的关键。该方法能保证永磁同步电动机起动平稳以及转子位置检测精度高。

关键词: 无传感器控制, 无迹卡尔曼滤波器, 滑模控制, 内置式永磁同步电机

Abstract:

Traditional sensorless techniques show quite unsatisfactory performance during start-up or transients in low speed range,therefore some new sensorless approaches without any additional start-up algorithms are needed. A special and new type of estimator for sensorless operation of an interior permanent magnet synchronous motor was studied.The algorithm of the UKF(unscented Kalman filter) was reviewed and its adaptation to the IPMSM d-q reference frame was explained.For evaluation of the drive performance, a comparison with sliding mode observer based estimation method was made.The simulation results indicate that the unscented Kalman filter provides more precise estimate of the rotor position and shows better performance at low speed range,however the initial position of the rotor is still required to guarantee the successful start-up of a motor.The introduced method can guarantee PMSM starting smoothly and the high accuracy of PMSM rotor position detection.

Key words: sensorless control, unscented Kalman filter (UKF), sliding mode observer, interior permanent magnet synchronous motor (IPMSM)

中图分类号:

TM351

SHEIANOV Aleksandr,康尔良. UKF估计器对IPMSM 无传感器控制的比较研究[J]. 微特电机, 2019, 47(4): 56-60.

SHEIANOV Aleksandr,KANG Er-liang. Comparative Study on Sensorless Control of IPMSM with UKF Estimator[J]. Small & Special Electrical Machines, 2019, 47(4): 56-60.

参考文献

[1]	SAYEE KRISHNA A V, KUNAR S. Position-sensorless operation of brushless permanent-magnet machines:A review [C]//IEEE International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE).Coimbatore, India, 2014.
[2]	Vas P . Sensorless vector and direct torque control[M]. Oxford: OUP Oxford, 1998: 263.
[3]	KALMAN R E . A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960,82(1):35.
[4]	SIMON D . Optimal state estimation[M]. Hoboken, New Jersey:John Wiley & Sons, 2006: 395.
[5]	DHAOUADI R, MOHAN N, NORMU L . Design and implementation of an extended Kalman filter for the state estimation of a permanent magnet synchronous motor[J]. IEEE Transactions on Power Electronics, 1991,6(3):491.
[6]	JULIER S J, UHLMANN J K. A new extension of the Kalman filter to nonlinear systems [C]//Signal Processing,Sensor Fusion,and Target Recognition VI. Orlando, FL,United States:AeroSense, 1997: 182.
[7]	VAN DER MERWE R, WAN E A. Efficient derivative-free Kalman filters for online learning [C]//European Symposium on Artificial Neural Networks (ESANN).Bruges,Blegium, 2001: 205.
[8]	STENGER B , MENDONCA P R S,CIPOLLA R.Model-based hand tracking using an unscented Kalman filter[C]//Proceedings of the British Machine Conference. Manchester: BMVA Press, 2001: 63.
[9]	AKIN B, ORGUNER U, ERSAK A. State estimation of induction motor using unscented Kalman filter [C]// IEEE Conference on Control Applications. Istanbul, Turkey: IEEE, 2003: 915.
[10]	BORSJE P, CHAN T F, WONG Y K, et al. A comparative study of Kalman filtering for sensorless control of a permanent-magnet synchronous motor drive [C]//IEEE International Conference on Electric Machines and Drives. San Antonio,TX,USA, 2005: 815.
[11]	TITAOUINE A, TAIBI D, BENNIS O, et al. Adaptive nonlinear control combined with unscented Kalman filter for permanent magnet synchronous motor fed by AC/DC/AC converter [C]//IEEE International Aegean Conference on Electrical Machines and Power Electronics and Electromotion. Istanbul, Turkey. 2011: 26.
[12]	Cheol Moon, Kee Hyun Nam, Mun Kyu Jung, et al. Sensorless speed control of permanent magnet synchronous motor using unscented Kalman filter [C]//IEEE SICE Annual Conference. Akita, Japan. 2012: 2018.
[13]	GUO Z Q, Sanjib Kumar Panda. Design of a sliding mode observer for sensor less control of SPMSM operating at medium and high speeds [C]//Sensorless Control for Electrical Drives (SLED).Sydney,NSW,Australia.IEEE, 2015: 1.
[14]	WANG Z H . A simple startup strategy based on current regulation for back-emf-based sensorless control of PMSM[J]. IEEE Transactions on Power Electronics, 2012,27(8):3817.
[15]	TAO P, LIN J X, CHEN Z. Ultra-low and zero speed sensorless control of permanent magnet synchronous motor based on sliding mode observer [C]//Advanced Information Technology,Electronic and Automation Control Conference (IAEAC). Chongqing,China:IEEE. 2015: 547.
[16]	YOU C J, LI J, HUANG X Y, et al. A full-speed sensorless control algorithm for interior permanent magnet synchronous motor using sliding-mode observer and hf signal injection [C]//Vehicle Power and Propulsion Conference (VPPC).Belfort,France.IEEE, 2017: 1.
[17]	JIN X H, NI R G, CHEN W, BLAABJERG F . High frequency voltage injection methods and observer design for initial position detection of permanent magnet synchronous machines[J]. IEEE Transactions on Power Electronics, 2017,33(9):7971.
[18]	ZHANG X, LI H Y . Initial rotor position estimation for IPMSM based on high frequency rotating voltage injection[J]. Transactions of China Electrotechnical Society, 2018,33(15):3547.
[19]	LI J, ZHOU B, LIU B, et al. A novel strategy of initial rotor position detection for surface mounted permanent magnet synchronous machines [C]//IEEE 17th International Conference on Electrical Machines and Systems (ICEMS).Hangzhou,China. 2014: 733.
[20]	YANG S C, HSU Y L, CHEN G R. Design issues for permanent magnet machine sensorless drive combining saliency-based and back-emf-based control in the entire speed range [C]//IEEE International Conference on Industrial Technology (ICIT). Taipei,Taiwan. 2016: 1270.
[21]	DARBA A, SALAHSHOOR K. EKF and UKF-based estimation of a sensorless axial flux pm machine under an internal-model control scheme using a svpwm inverter [C]//IEEE Proceedings of the 29th Chinese Control Conference. Beijing, China. 2010: 5676.