A Robustness-Improved Data-Driven Predictive Current Control for SPMSM Drives Through Iteratively Reweighted Least Square Solution

作者：Zichun Tang, Chenwei Ma, Wensheng Song, Jiayao Li, Hao Yue · 发表于：IEEE Transactions on Transportation Electrification · 年份：2025 · DOI：10.1109/tte.2025.3589779 · 被引用次数：1 · 研究领域：Multilevel Inverters and Converters、Microgrid Control and Optimization、Advanced DC-DC Converters

Model predictive control (MPC) techniques in machine drives typically rely on the necessity of a preknown accurate mathematic model. However, the strong time-varying characteristics of machine parameters with parasitic effect may significantly affect the performance of MPC schemes. The least-squares (LS) algorithm has been introduced to machine drives to avoid the dependence on accurate models for MPC. However, such the LS solution could be unreliable in the presence of noisy measurements. To address this issue, this article proposes a robustness-improved data-driven predictive current control method utilizing iteratively reweighted LS (IRLS) solution for surface-mounted permanent magnet synchronous machine (SPMSM) drives. The proposed method can effectively avoid the influence of system noise on the performance of the LS model, due to dynamic weighting of measurement data with an online evaluation of current prediction residuals. It does not require any offline training or parameter configurations with exceptional generalization ability. Experimental validation on a SPMSM test bench demonstrates the superior robustness and satisfactory control performance, especially during transient and at low-speed steady-state operations, compared to the conventional LS approaches.