基于SMC和AFEKF的PMSM无传感控制
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摘要
针对传统扩展卡尔曼滤波(EKF)估计永磁同步电机(PMSM)速度、位置存在模型不精确,噪声不确定时估计精度不高、实时性差,且有可能导致滤波发散的问题,采用一种基于Sage-Husa的自适应渐消扩展卡尔曼滤波(AFEKF)算法。为了提高模型误差鲁棒性、减少超调,采用一种新型的基于指数趋近律的滑模转速控制器(SMC)。实验结果表明:所提出的控制策略相较于传统的比例积分(PI)和EKF算法能准确估计转速和位置。在启动时能较快到达预定速度,且无超调现象,迅速达到稳定状态;在加载后最大偏差较传统算法减小了1. 77%,且稳定状态下,转速误差下降了0. 371%,稳态位置误差减小了0. 45%。证实所提算法在PMSM无传感器控制系统中稳定性强,具有更好的实用性。
Aiming at problem that while traditional extended Kalman filter( EKF) is used to estimate velocity and position of permanent magnet synchronous motor( PMSM),the model is not accurate,the estimation precision is not high and the real-time performance is poor when the noise is uncertain,and it may lead to filtering divergence,a Sage-Husa adaptive fading extended Kalman filtering( AFEKF) algorithm is adopted. In order to improve the robustness of model errors and reduce overshoot,a new type of sliding-mode speed controller( SMC) based on exponential reaching law is adopted. The experimental result shows that,the proposed control strategy can estimate the rotating speed and position accurately compared to the traditional proportional integral( PI) and EKF algorithms. At the start time,it can reach the preset speed quickly,and has no overshoot phenomenon,and quickly reaches the stable state; the maximum deviation after loading is reduced by 1. 77 % compared with traditional algorithm,and the rotating speed error is decreased by 0. 371 % in steady state,and the steady-state position error is decreased by 0. 45 %. It is proved that the algorithm is stable and practical in PMSM sensorless control system.
Aiming at problem that while traditional extended Kalman filter( EKF) is used to estimate velocity and position of permanent magnet synchronous motor( PMSM),the model is not accurate,the estimation precision is not high and the real-time performance is poor when the noise is uncertain,and it may lead to filtering divergence,a Sage-Husa adaptive fading extended Kalman filtering( AFEKF) algorithm is adopted. In order to improve the robustness of model errors and reduce overshoot,a new type of sliding-mode speed controller( SMC) based on exponential reaching law is adopted. The experimental result shows that,the proposed control strategy can estimate the rotating speed and position accurately compared to the traditional proportional integral( PI) and EKF algorithms. At the start time,it can reach the preset speed quickly,and has no overshoot phenomenon,and quickly reaches the stable state; the maximum deviation after loading is reduced by 1. 77 % compared with traditional algorithm,and the rotating speed error is decreased by 0. 371 % in steady state,and the steady-state position error is decreased by 0. 45 %. It is proved that the algorithm is stable and practical in PMSM sensorless control system.
引文
[1]易伯瑜.永磁同步电动机高性能无传感器控制技术研究[D].广州:华南理工大学,2014.
[2]莫会成,闵琳.现代高性能永磁交流伺服系统综述—传感装置与技术篇[J].电工技术学报,2015,30(6):10-21.
[3] Kim H,Son J,Lee J. A high-peed sliding-mode observer for the sensorless speed control of a PMSM[J]. IEEE Transactions on Industrial Electronics,2011,58(9):4069-4077.
[4]杨圣蓉,王剑平,王思,等.基于粒子群优化算法的无传感器调速系统研究[J].传感器与微系统,2015,34(7):26-28,32.
[5] Lee Guen-Bo,Kwon Young-Ahn. High-performance sensorlesscontrol of PMSM using back-EMF and reactive power[J]. Transactions of the Korean Institute of Electrical Engineers,2010,59(4):740-742.
[6] Liu J M,Zhu Z Q. Sensorless control strategy by square-waveform high-frequency pulsating signal injection into stationary reference frame[J]. IEEE Journal of Volume,2014,2(2):171-180.
[7] Kulkarni A B. A novel position sensor elimination technique for the interior permanent-magnet synchronous motor drives[J].IEEE Trans on Industry Applications,2012,28(1):144-150.
[8] Hicham Chaoui,Pierre Sicard. Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines[J]. Neural Computing and Applications,2011,20(5):717-727.
[9]付兴武,黄劲松,王怀震,等.无速度传感器电励磁同步电机模型预测电流控制[J].传感器与微系统,2017,36(8):39-41.
[10]张勇刚,李宁,奔粤阳.最优状态估计[M].北京:国防工业出版社,2013:88-103.
[11]余跃,何凤有,鲍卫宁.基于渐消卡尔曼滤波器的永磁同步电机的仿真[J].微特电机,2011,39(6):12-14.
[12]付梦印,邓志红,闫莉萍. Kalman滤波理论及其在导航系统中的应用[M].北京:科学出版社,2010:112-143.
[13]方斯琛,周波,黄佳佳,等.滑模控制永磁同步电动机调速系统[J].电工技术学报,2008,23(8):29-35.
[14]张晓光,赵克,孙力,等.永磁同步电动机滑模变结构调速系统新型趋近率控制[J].中国电机工程报,2011,31(24):77-82.
[15] Kung Y. Simulink/Modelsim Co-simulation of EKF-based sensorless PMSM drives[C]∥2013 IEEE the 10th International Conference on Power Electronics and Drive Systems(PEDS),2013.
[16]潘倩兮,赵龙,张常云. INS/GPS组合导航中带有置信度的抗差Kalman滤波算法[C]∥2011年中国智能自动化学术会议,中国,北京,2011.
[2]莫会成,闵琳.现代高性能永磁交流伺服系统综述—传感装置与技术篇[J].电工技术学报,2015,30(6):10-21.
[3] Kim H,Son J,Lee J. A high-peed sliding-mode observer for the sensorless speed control of a PMSM[J]. IEEE Transactions on Industrial Electronics,2011,58(9):4069-4077.
[4]杨圣蓉,王剑平,王思,等.基于粒子群优化算法的无传感器调速系统研究[J].传感器与微系统,2015,34(7):26-28,32.
[5] Lee Guen-Bo,Kwon Young-Ahn. High-performance sensorlesscontrol of PMSM using back-EMF and reactive power[J]. Transactions of the Korean Institute of Electrical Engineers,2010,59(4):740-742.
[6] Liu J M,Zhu Z Q. Sensorless control strategy by square-waveform high-frequency pulsating signal injection into stationary reference frame[J]. IEEE Journal of Volume,2014,2(2):171-180.
[7] Kulkarni A B. A novel position sensor elimination technique for the interior permanent-magnet synchronous motor drives[J].IEEE Trans on Industry Applications,2012,28(1):144-150.
[8] Hicham Chaoui,Pierre Sicard. Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines[J]. Neural Computing and Applications,2011,20(5):717-727.
[9]付兴武,黄劲松,王怀震,等.无速度传感器电励磁同步电机模型预测电流控制[J].传感器与微系统,2017,36(8):39-41.
[10]张勇刚,李宁,奔粤阳.最优状态估计[M].北京:国防工业出版社,2013:88-103.
[11]余跃,何凤有,鲍卫宁.基于渐消卡尔曼滤波器的永磁同步电机的仿真[J].微特电机,2011,39(6):12-14.
[12]付梦印,邓志红,闫莉萍. Kalman滤波理论及其在导航系统中的应用[M].北京:科学出版社,2010:112-143.
[13]方斯琛,周波,黄佳佳,等.滑模控制永磁同步电动机调速系统[J].电工技术学报,2008,23(8):29-35.
[14]张晓光,赵克,孙力,等.永磁同步电动机滑模变结构调速系统新型趋近率控制[J].中国电机工程报,2011,31(24):77-82.
[15] Kung Y. Simulink/Modelsim Co-simulation of EKF-based sensorless PMSM drives[C]∥2013 IEEE the 10th International Conference on Power Electronics and Drive Systems(PEDS),2013.
[16]潘倩兮,赵龙,张常云. INS/GPS组合导航中带有置信度的抗差Kalman滤波算法[C]∥2011年中国智能自动化学术会议,中国,北京,2011.