针对电机运行过程中参数变化会影响永磁同步电机(PMSM)无位置传感器控制性能的问题，将递推的最小二乘法(RLS)用于PMSM参数的在线辨识，在最大转矩电流比控制策略下，使用基于BP神经网络改进的模型参考自适应系统构建无位置传感器控制方案，提出了基于在线参数辨识的PMSM无位置传感器控制方案。运用递推的RLS对PMSM的交轴电感和转子磁链进行在线辨识，并将参数辨识结果应用于电机无位置传感器算法中。仿真和试验证明了基于递推的RLS参数辨识算法可以对PMSM的转子磁链和交轴电感值进行准确辨识，基于参数辨识的PMSM无位置传感器控制方案性能更好。

The change of motor parameters can affect the performance of permanent magnet synchronous motor (PMSM) position sensorless control. In order to solve this problem, the recursive least squares (RLS) algorithm is used for the online identification of PMSM parameters. Under the maximum torque per ampere (MTPA) control strategy, the improved model reference adaptive system (MRAS) based on back propagation (BP) neural network is used to construct a position sensorless control scheme, and a position sensorless control scheme for PMSMs based on parameter identification is proposed. The RLS method is used to identify the quadrature axis inductance and rotor flux of the PMSM online, and the parameter identification results are applied to the PMSM sensorless algorithm. Simulations and experiments prove that the parameter identification algorithm based on the RLS method can accurately identify the rotor flux and quadrature axis inductance of the PMSM, and the PMSM sensorless control scheme based on parameter identification is feasible.

国家自然科学基金项目(52077140)