基于观测器的逆变器特性自学习与补偿控制

Observer-based inverter characteristic self-learning and compensation control

针对交流电机的控制过程中,逆变器的干扰电压存在复杂的非线性特性,直接补偿逆变器互锁时间难以实现精确的电压输出控制,故一般需要对逆变器的干扰特征进行离线标定的缺点,应用了一种利用观测器自学习逆变器干扰特性并进行补偿的方法,无需离线标定,可以很好地获取逆变器的干扰特性,同时实现高性能的逆变器干扰补偿控制。通过将不同补偿函数在一台1 kW永磁同步电机上进行实验研究,验证了本文方法对逆变器干扰估计和补偿的有效性和准确性。

In the control of AC motors,the inverter output voltage is difficult to be accurately controlled due to the inverter disturbance,which affects the output torque and efficiency.The inverter disturbance has complex nonlinear characteristics,so it is difficult to be well compensated by simply using the inverter interlock time.Consequently,offline inverter disturbance calibration is required.In this paper,an observerbased inverter disturbance self-learning and compensation method is implemented.Inverter disturbance characteristics can be accurately obtained without offline calibration.At the same time,high-performance inverter disturbance compensation is realized.By implementing proposed compensation methods to a 1 kW prototype permanent magnet synchronous motor,the performance of the inverter disturbance self-learning and compensation are both verified.