基于人工神经网络的永磁电机无传感器控制与实现

Design and Realization of Sensorless Control of Permanent Magnet Synchronous Motor Based on Artificial Neural Network

无传感器控制可以降低永磁电机的成本,并且提高系统的可靠性。永磁电机的电感等参数受运行工况影响会发生变化,因而准确识别电机参数对提高电机的控制精度具有积极意义。基于永磁电机的电感模型,采用人工神经网络对电机电感进行参数辨识,再通过滑模观测器对转子角度进行观测,实现永磁电机无位置传感器的矢量控制。利用TI公司的TMS320F28379d DSP作为控制芯片,搭建了电机控制电路,对所提控制策略进行了验证。结果表明,该控制策略能实时准确地辨识电机参数,提高控制精度,改善控制性能。

Sensorless control can reduce the cost of permanent magnet motor and improve the reliability of the system. Because the inductance and other parameters of permanent magnet motor change with the operating conditions, the accurate identification of motor parameters has a positive significance to improve the control accuracy of motor. Based on the mathematical model of permanent magnet motor, the parameters of motor inductance are identified by artificial neural network, and then the rotor angle is observed by sliding mode observer(SMO) to realize the sensorless vector control of permanent magnet motor. Using TI′s TMS320 F28379 d DSP as the control chip, a motor control circuit is built to verify the above control strategy. The results show that the control strategy can accurately identify the motor parameters in real time, as well as improve the control precision and performance.