基于EKF的PMSM无传感器控制研究
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摘要
针对永磁同步电机(PMSM)位置与速度传感器易受外部条件和自身精度的影响,以及PMSM无传感器控制等问题,提出了一种基于扩展卡尔曼滤波(EKF)的PMSM非线性预测无传感器控制方法。该方法具有预测性、自适应能力、抗干扰性、易于软件实现等优点。首先,详细分析了PMSM的矢量控制系统数学模型和EKF原理。其次,将EKF算法应用于PMSM的无传感器矢量控制中,即将电机αβ轴电流和电压作为输入变量,经过EKF算法运算,估算出转子转速和转子位置来代替电机的位置与速度传感器。最后,搭建基于MATLAB/Simulink的PMSM无传感器矢量控制系统仿真模型。仿真结果表明,EKF控制方法能准确估算出电机在空载和负载(随机)时的位置和转速,且具有较好的可预测性和系统响应性。在电机突加负载的情况下,也可以快速恢复到稳定状态,具有较强的抗负载性。
Aiming at the position and speed sensors of permanent magnet synchronous motor( PMSM) are susceptible to external conditions and their own accuracy,as well as the PMSM sensorless control,a nonlinear predictive control approach based on extended Kalman filter( EKF) is proposed. The method has the advantages of predictability,adaptability,anti-interference,and ease in software implementation. First,the mathematical model of vector control system of PMSM and principle of EKF are analyzed in detail. Then,EKF algorithm is applied to sensorless vector control of PMSM,i. e.,the motor αβ shaft current and voltage are taken as input variables,and after EKF algorithm calculation,the rotor speed and rotor position are estimated to replace the motor position and speed sensor. Finally,a simulation model based on MATLAB/Simulink is built. Simulation results show that the EKF control method can accurately estimate the motor position and speed under no-load and load conditions with good predictability and system responsiveness. In the case of a sudden load put on the motor,the motor can quickly resume to a stable state,with strong anti-load resistance capability.
Aiming at the position and speed sensors of permanent magnet synchronous motor( PMSM) are susceptible to external conditions and their own accuracy,as well as the PMSM sensorless control,a nonlinear predictive control approach based on extended Kalman filter( EKF) is proposed. The method has the advantages of predictability,adaptability,anti-interference,and ease in software implementation. First,the mathematical model of vector control system of PMSM and principle of EKF are analyzed in detail. Then,EKF algorithm is applied to sensorless vector control of PMSM,i. e.,the motor αβ shaft current and voltage are taken as input variables,and after EKF algorithm calculation,the rotor speed and rotor position are estimated to replace the motor position and speed sensor. Finally,a simulation model based on MATLAB/Simulink is built. Simulation results show that the EKF control method can accurately estimate the motor position and speed under no-load and load conditions with good predictability and system responsiveness. In the case of a sudden load put on the motor,the motor can quickly resume to a stable state,with strong anti-load resistance capability.
引文
[1]柏达.基于滑模变结构的永磁同步电机调速系统研究[D].长沙:湖南科技大学,2017.
[2]高金锁,谢明,朱强.基于EKF的永磁同步电机的无传感器控制研究[J].电子科技,2017,30(12):55-58.
[3]黄胜军,吴峻,汤钧元,等.基于锁相环的永磁同步电机无传感器位置检测[J].电力电子技术,2018,52(2):47-49.
[4]朱晓虹,张广明,梅磊,等.基于滑模速度控制器的PMSM无速度传感器控制研究[J].电机与控制应用,2016(1):1-6.
[5]薛树功,魏利胜,凌有铸.基于扩展卡尔曼滤波的永磁同步电机无传感器矢量控制[J].电机与控制应用,2011(8):15-18.
[6]刘计龙,肖飞,沈洋,等.永磁同步电机无位置传感器控制技术研究综述[J].电工技术学报,2017,32(16):76-88.
[7]付光杰,林雨晴,牟海维.神经网络预测在无刷直流电机调速中的应用[J].自动化仪表,2017,38(4):9-12.
[8]张炳兰.改进遗传算法PID在电液伺服系统的应用[J].自动化仪表,2017,38(8):28-32.
[9]廖自力,陈路明.永磁同步电机矢量控制发展综述[J].工程技术,2016(2):181-182.
[10]庞晴晴.永磁同步电机无传感器控制技术研究[D].徐州:中国矿业大学,2011.
[11]李英强,杨明,龙江,刘子锐,等.基于扩展卡尔曼滤波的永磁同步电机无电流传感器预测控制[J].电机与控制应用,2018,45(1):107-113.
[12]孙旭霞,刘博.基于EKF的PMSM无传感器控制及滤波参数选取[J].微特电机,2011(5):39-41.
[2]高金锁,谢明,朱强.基于EKF的永磁同步电机的无传感器控制研究[J].电子科技,2017,30(12):55-58.
[3]黄胜军,吴峻,汤钧元,等.基于锁相环的永磁同步电机无传感器位置检测[J].电力电子技术,2018,52(2):47-49.
[4]朱晓虹,张广明,梅磊,等.基于滑模速度控制器的PMSM无速度传感器控制研究[J].电机与控制应用,2016(1):1-6.
[5]薛树功,魏利胜,凌有铸.基于扩展卡尔曼滤波的永磁同步电机无传感器矢量控制[J].电机与控制应用,2011(8):15-18.
[6]刘计龙,肖飞,沈洋,等.永磁同步电机无位置传感器控制技术研究综述[J].电工技术学报,2017,32(16):76-88.
[7]付光杰,林雨晴,牟海维.神经网络预测在无刷直流电机调速中的应用[J].自动化仪表,2017,38(4):9-12.
[8]张炳兰.改进遗传算法PID在电液伺服系统的应用[J].自动化仪表,2017,38(8):28-32.
[9]廖自力,陈路明.永磁同步电机矢量控制发展综述[J].工程技术,2016(2):181-182.
[10]庞晴晴.永磁同步电机无传感器控制技术研究[D].徐州:中国矿业大学,2011.
[11]李英强,杨明,龙江,刘子锐,等.基于扩展卡尔曼滤波的永磁同步电机无电流传感器预测控制[J].电机与控制应用,2018,45(1):107-113.
[12]孙旭霞,刘博.基于EKF的PMSM无传感器控制及滤波参数选取[J].微特电机,2011(5):39-41.