双扩展卡尔曼滤波的无刷直流电机无传感器控制

Applying Dual Extended Kalman Filter(DEKF) Theory to Sensorless Control of Brushless DC Motor(BLDCM)

文章研究了双扩展卡尔曼滤波器(DEKF)下的无刷直流电机(BLDCM)无位置传感器控制策略。利用带有噪声的输入信号,设计了一种DEKF观测器以实时估计无刷直流电机状态和参数。在观测器中,2个扩展卡尔曼滤波器分别作为状态滤波器和参数滤波器同时运行估计系统状态和电机参数。在任意时刻下,当前参数估计值作为状态滤波器的一个已知给定输入量,与此同时,当前状态估计值也作为参数滤波器的一个已知给定输入量。所设计的观测器对参数变化、模型不精确、过程噪声和测量噪声具有较强的鲁棒性,在稳态运行和动态运行模式下都可以获得足够精度的BLDCM状态和参数估计值。仿真研究进一步验证了所提出控制策略的可行性和有效性。

Aim.To our knowledge,no paper in the open literature has applied the DEKF theory to the sensorless control of BLDCM.Subsections 2.1,2.2 and 2.3 of the full paper explain our new method.Subsection 2.1 explains the principles of the DEKF with the help of Fig.1.Subsection 2.2 does two things：（1） it designs the observer which contains two extended Kalman filters（EKFs）;the two EKFs estimate simultaneously states（rotor speed and rotor position） of BLDCM and its parameters（stator resistance and stator inductance） respectively;（2） at every update of states,the estimated current parameters are used as given inputs to filter the states,and likewise,the estimated current states are used to filter the parameters.With the help of Fig.2,subsection 2.3 discusses the sensorless control scheme of the BLDCM that contains the observer we designed.Subsection 2.4 presents the simulation results as given in Fig.4.The results show preliminarily that：（1） the observer is robust to parameter variations,model inaccuracies,process noise and measurement noise;（2） it has high accuracy estimation performance for the states and parameters of BLDCM during both steady-state and dynamic modes of operation.