高效能同步磁阻电机数据驱动型模型预测控制方法

Data-Driven Model Predictive Control for High Performance Synchronous Reluctance Motor

结合电力电子系统有限开关状态特性,近年来有限状态集模型预测控制(FCS-MPC)利用被控对象离散预测模型在同步磁阻电机(SynRM)能效提升系统中得到广泛应用。然而,FCS-MPC建模过程严重依赖电机数学模型,电机参数精确性会直接影响系统控制效果。为解决上述问题,提出一种数据驱动型模型预测控制(DD-MPC)方法。该方法不利用任何电机数学模型信息,仅基于电机运行时的输入输出数据关系,通过对数据的处理分析实现“边建模、边控制”。此外,为保证DD-MPC系统的收敛性和稳定性,设计了一种DD-MPC输入输出数据关系的高更新率实现方法,利用最近输出的三电压矢量对应电流变化信息,推演并更新全局电压矢量对应电流变化关系。最后,基于25 kW样机对DD-MPC控制效果进行测试与分析。与传统FCS-MPC方法对比,所提方法在保留FCS-MPC快速性和灵活性等优势的基础上,可有效提升系统的鲁棒性和稳定性。

In recent years,using the discrete model of the object and considering the finite switching state characteristics of power electronic systems,finite control set model predictive control(FCS-MPC)has been widely used in synchronous reluctance motor(SynRM)drive system to promote the energy efficiency.However,the FCS-MPC modeling process heavily depends on the SynRM mathematical model,and the accuracy of motor parameters directly affects the control effect.To solve the above problems,a data-driven MPC(DD-MPC)method is proposed.This method does not need the SynRM mathematical model information.It only utilizes the input-output data relationship of the SynRM.So the“synchronization of modeling and control”is realized.In addition,in order to ensure the convergence and stability of DD-MPC system,a DD-MPC data relation with high update rate is designed.By analyzing the current change information corresponding to the recent three-voltage-vector output,the corresponding current change relation of the global voltage vectors is deduced and updated.Finally,the control effect of DD-MPC is tested and analyzed based on a 25 kW prototype.Compared with the traditional FCS-MPC,the proposed method can effectively improve the robustness and stability,while maintaining the rapidity and flexibility of FCS-MPC.