Model Predictive Control Strategy for Open-Winding Motor System Based on ResNet

Open-winding permanent-magnet synchronous motors feature flexible control and a high fault-tolerance capability, making them widely used in high-reliability and high-power scenarios such as military equipment and electric locomotives. To address the issues that traditional model predictive control fails to balance, such as zero-sequence current suppression, system loss optimization and the reliance of weight parameter design on experience (with online optimization consuming excessive resources), this paper proposes an OW-PMSM MPC strategy for loss optimization and a weight design method based on a residual neural network. Specifically, the former strategy adds a zero-sequence current suppression term and a loss quantification term to the MPC cost function, enabling coordinated control of the two objectives; the latter establishes a mapping between weight parameters and motor performance via ResNet (which avoids the gradient vanishing problem in deep networks) and outputs optimal weight parameters offline to save online computing resources. Comparative experiments under two operating conditions show that the improved MPC strategy reduces system loss by 25%, while the ResNet-based weight design improves the performance of the drive system by 30%, fully verifying the effectiveness of the proposed methods.