无刷直流电机转矩观测与电感自适应辨识

Torque Estimation and Adaptive Inductance Identification for a Brushless DC Motor

为提高无刷直流电机(brushless DC motor,BLDCM)转矩观测结果的准确性,定量分析了传统的反电势滑模观测器(sliding-mode observer,SMO)及相应的转矩观测结果受定子电感参数偏差的影响程度。分析表明,电感参数偏差会在定子电流变化时引起反电势和转矩观测误差。为消除这一影响,利用李雅普诺夫(Lyapunov)稳定性理论,设计了定子电感自适应辨识率,在线辨识得到电感参数,实时调整SMO的系数矩阵和输入矩阵,构成了新型的自适应滑模观测器,提高了观测精度。最后利用RT-LAB实时控制器进行实验,验证了理论分析的正确性。实验结果表明,所提方法能有效辨识出BLDCM的定子电感值,并正确观测出电机转矩。

To improve the accuracy of torque observation result for a brushless DC motor （BLDCM）, the influences of stator inductance deviation on traditional back electromotive force （EMF） sliding-mode observer （SMO） and relevant torque observation results were quantitatively analyzed. The analysis results show the observation errors of back EMF and torque are induced by the inductance deviation when the stator currents change. To eliminate this effect, an adaptive law for stator inductance identification was designed based on Lyapunov stability theory and the online identification result was used to regulate the coefficient matrix and input matrix of SMO in real time. Combining with the inductance estimator, a novel adaptive sliding-mode observer was constructed. Further, the precision of SMO was enhanced. Experimental results, using RT-LAB real time controller implementation, verify the validity of the theoretical analysis and prove that the stator inductance can be identified effectively and the torque of BLDCM can be observed correctly by the proposed method.