Torque-tracking MTPA control strategy of permanent magnet synchronous motors based on machine learning regularization theory

Maximum torque per ampere (MTPA) in internal permanent magnet synchronous motor (IPMSM) is a classical problem in AC motor control. The control strategy of IPMSM for electric vehicle not only need to achieve the MTPA, but also need to accurately track the torque commands. In order to solve above problem, a regularization concept from machine learning theory was introduced to transform the traditional MTPA problem into L1 and L2 regularization issues. Firstly, the MTPA control problem is equivalent to the L2 regularization issue, and then the L1 regularization torque modeling was carried out for the torque tracking. Finally, the Lagrange dual method was used to optimize the above regularization-based MTPA problem. Theoretical and experimental analysis show that the proposed method can achieve an optimal current distribution scheme which both the maximum torque current ratio and the high torque tracking accuracy can be considered. Moreover, the proposed method solves the problem in a simple and analytical manner, and the solution is easy to be interpreted. Thus, it combines the advantages of model-driven and data-driven methods. © 2023 Editorial Department of Electric Machines and Control. All rights reserved.