Electromagnetic Performance Evaluation and Lookup-Table-Based TD3 Current Control of a Nonlinear PMSM

Conventional proportional integral (PI) current controllers used in field-oriented control (FOC) have an acceptable performance under linear motor models, but their performance degrades when nonlinear machine characteristics are taken into account. This paper presents a comparative evaluation of PI and twin delayed deep deterministic policy gradient (TD3)-based current control for a permanent magnet synchronous motor (PMSM) drive using a high-fidelity nonlinear motor model. The first step involves modeling and validating a PMSM in JMAG using finite element analysis, assessing its electromagnetic properties. Subsequently, JMAG-RT is utilized to generate nonlinear flux, torque, and inductance maps, which are implemented as lookup tables (LUTs) in MATLAB/Simulink (R2026a) to accurately capture the motor’s nonlinear behavior. Then, the performance of both the PI and TD3 controllers is tested with both lumped-parameter and nonlinear motor models. The results show that the PI controller has strong speed oscillation and current fluctuation under nonlinear operating conditions, but the TD3-based controller has better tracking accuracy and current regulation performance. In terms of integral absolute error (IAE), compared to PI-based nonlinear motor parameters, the TD3 control framework provides a 17.6% reduction in speed IAE, and a minor 0.4% decrease in q-axis current IAE, resulting in accurate reference tracking under nonlinear operating conditions. These findings demonstrate the effectiveness of reinforcement learning for enhancing the performance of PMSM drives when high-fidelity nonlinear motor models are considered.