Deadbeat Predictive Current Control Based on Online Parameter Identification

本文主要研究表贴式永磁同步电机的无差拍预测电流控制(DPCC)，并提出了一种考虑采样延迟的控制策略，以克服数字控制中的延迟问题。模型参数不匹配是影响该控制方法广泛应用的主要障碍之一。为解决这一问题，本文提出了一种基于在线参数辨识的DPCC方案，通过模型参考自适应方法辨识电阻和电感，并结合q轴电流误差来辨识磁链参数，从而实现电机参数的动态更新，提升DPCC系统对电机参数变化的鲁棒性。为验证所提方案的有效性，进行了MATLAB/SIMULINK平台的仿真，并与传统DPCC进行了对比分析。结果表明，所提出的方案能够准确辨识定子电阻、电感及磁链参数，并有效抑制参数失配对DPCC系统性能的影响。This paper primarily studies the direct predictive current control (DPCC) for surface-mounted permanent magnet synchronous motors (SPMSM) and proposes a control strategy that considers sampling delay to overcome the delay issues in digital control. Parameter mismatch is one of the main obstacles affecting the widespread application of this control method. To address this issue, this paper presents an online parameter identification-based DPCC scheme. The scheme utilizes a model reference adaptive method to identify resistance and inductance, and combines the q-axis current error to identify the magnetic flux parameters, enabling dynamic updates of motor parameters and enhancing the robustness of the DPCC system against variations in motor para- meters. To validate the effectiveness of the proposed scheme, simulations were conducted on the MATLAB/SIMULINK platform and compared with traditional DPCC. The results demonstrate that the proposed scheme can accurately identify the stator resistance, inductance, and magnetic flux parameters, effectively mitigating the impact of parameter mismatch on the performance of the DPCC system.