永磁同步电机的转动惯量辨识及状态估计

Identification of inertia and state estimation for PMSM

为提高永磁同步电机伺服系统的动态性能和鲁棒性,研究了基于模型参考自适应系统的转动惯量辨识方法以及基于卡尔曼滤波器的自适应状态估计策略。提出了一种适用于宽转速、高噪声环境下的电机角速度、角位移和负载扰动转矩的在线估计方法,分析了该方法的抗干扰能力以及系统参数变化对估计效果的影响,并通过辨识出的伺服系统转动惯量对卡尔曼滤波器的系数矩阵进行实时更新,实现了转动惯量自适应状态估计。仿真和实验结果表明该算法在速度分辨率、实时性和抗干扰能力上均优于传统M/T方法。

Based on theories of the model reference adaptive system （MRAS） and the Kalman filter, the online inertia identification and state estimation of permanent magnet synchronous motor （PMSM） servo system were respectively studied for improving the dynamic performance and robustness. In the proposed algorithm, an optimal state estimator based on the Kalman filter was used to provide exact estimation for the rotor speed, rotor position and disturbance torque in a random noisy environment. Also, the MRAS was incorporated to identify the variations of inertia moment real time, and the identified inertia was used to adapt the EKF for better dynamic performance. In addition, the disturbancerejection ability to variations of the mechanical parameters was discussed, and it was verified that the system was robust to the modeling error and system noise. Simulation and experimental results showed that, compared with the M/T method, the proposed technique had better performance in speed resolution, real-time and anti-interference ability.