Adaptive Ridge Regression-Based Data-Driven Current Prediction Modeling for PMSM Drives With High Robustness Against Data Noises

This article presents an adaptive ridge regression (ARR)-based data-driven current prediction model aimed at enhancing robustness in permanent magnet synchronous motor (PMSM) drives. Although least squares (LS) methods have displayed certain potential for improving parameter robustness in model predictive control (MPC) applications, the influence of the inevitable data noises has not been fully considered in such a data-based method. Aiming to improve the robustness against data noises, an adaptive ridge regression (ARR) data-driven current prediction is proposed for permanent magnet synchronous machine (PMSM) drives in this article. The proposed method introduces an online evaluation of data noise severity through a variance inflation factor (VIF). A dynamic ridge coefficient adjustment mechanism according to the VIF is then applied. As a result, an adaptive penalization to the data noises is achieved, leading to an improved robustness. Experimental verification on a PMSM drive system shows improvements compared to conventional LS methods, especially under challenging low-speed operating conditions where noise sensitivity is most evident. The proposed ARR method preserves the parameter-independent benefits of data-driven approaches while achieving superior current prediction accuracy in noisy settings, thereby advancing the practical implementation of robust MPC strategies for PMSM drives.