具有参数鲁棒性的永磁同步电机模型预测控制

Model predictive control of permanent magnet synchronous motor with parameter robustness

永磁同步电机有限集模型预测控制的预测结果高度依赖于系统模型参数,存在参数失配而导致控制性能下降的问题.为了使永磁同步电机模型预测控制具有更强的参数鲁棒性,降低其对参数的敏感性,提出一种基于增量式电流预测方程,采用改进模型参考自适应系统(MRAS)进行参数辨识的控制方法.为了减少系统对电机参数的依赖数量,引入增量式电流预测模型,消除了磁链参数,使其只包含电阻和电感参数.针对传统MRAS方法中出现的因欠秩而无法同时辨识三个参数的问题,采用了一种改进MRAS方法,该方法可在参数均存在失配情况下实现对电阻、电感参数的准确辨识.通过实验验证了所提方法的有效性.

The prediction results of finite set model predictive control for permanent magnet synchronous motors are highly dependent on the system model parameters,and there is a problem of parameter mismatch leading to a decrease in control performance.In order to enhance the parameter robustness of the permanent magnet synchronous motor model predictive control and reduce its sensitivity to parameters,a control method based on the incremental current prediction equation is proposed,which utilizes an improved model reference adaptive system(MRAS)for parameter identification.In order to reduce the dependence of the system on motor parameters,an incremental current prediction model is introduced,which eliminates the magnetic flux parameters and only includes the resistance and inductance parameters.In response to the problem that traditional MRAS methods cannot identify three parameters simultaneously due to lack of rank,an improved MRAS method is adopted,this method can accurately identify resistance and inductance parameters in the presence of parameter mismatch.The effectiveness of the proposed method is verified through experiments.