CAJ | 학술논문

传统的扩展卡尔曼滤波器(Extended Kalman Filter，EKF)用于无刷直流电机状态辨识时，观测数据容易出现残差，辨识结果偏差大，位置及转速存在耦合，导致辨识系统鲁棒性弱。文章基于离散的直流无刷电机(Brushless DC Moter，BLDCM)数学模型和 M-估计方法，构建了改进的扩展卡尔曼滤波算法(MEKF)。首先，基于 BLDCM的工作原理，建立了独立于 EKF的 BLDCM换相离散模型；其次，通过修正系统观测矩阵，对转速与位置的强耦合关系进行解耦，实现了 EKF分离变量辨识；最后，基于去耦合后的时序模型设计出独立于 EKF的转子位置检测模块，无需深度滤波就可实现转子的精确定位。实验仿真结果表明，文章方法能够有效抑制卡尔曼滤波器的粗差扰动，提高了系统抵抗初始值不确定性的干扰和系统鲁棒性。
전통적확전잡이만려파기(Extended Kalman Filter，EKF)용우무쇄직류전궤상태변식시，관측수거용역출현잔차，변식결과편차대，위치급전속존재우합，도치변식계통로봉성약。문장기우리산적직류무쇄전궤(Brushless DC Moter，BLDCM)수학모형화 M-고계방법，구건료개진적확전잡이만려파산법(MEKF)。수선，기우 BLDCM적공작원리，건립료독립우 EKF적 BLDCM환상리산모형；기차，통과수정계통관측구진，대전속여위치적강우합관계진행해우，실현료 EKF분리변량변식；최후，기우거우합후적시서모형설계출독립우 EKF적전자위치검측모괴，무수심도려파취가실현전자적정학정위。실험방진결과표명，문장방법능구유효억제잡이만려파기적조차우동，제고료계통저항초시치불학정성적간우화계통로봉성。
The traditional Extended Kalman Filter (EKF) will lead to large errors of estimation data and robustness weakening of the identiifcation system when applied to brushless DC motor (BLDCM) to estimate rotor position and speed simultaneously. In this paper, based on the discrete mathematical model of BLDCM and M-estimation, an improved EKF (MEKF) was proposed. Firstly, based on the commutation principle of the BLDCM operation and EKF model, a separate commutation model was built up, which was independent to the EKF. Secondly, in order to identify the rotor speed and position with more precision and to enhance the robustness of system, the observation matrix was modiifed by M-estimation, and a decoupling technology of speed and position was adopted in the system correspondingly. Thirdly, based on the decoupled time series model of the motor, a rotor position detection model was designed out, so that a precision rotor position can be realized in practice without deep ifltration which will lead to great lagging. The experiment and simulation results show that this method can effectively abate the errors disturbance of the EKF, and it also signiifcantly enhances the anti-interference of the initial value and robustness.