Model Predictive Current Control for Dynamic Enhancement in PMSM PHIL Simulation

This paper addresses the issue of insufficient dynamic performance in traditional PI control for power-level hardware-in-the-loop (PHIL) simulations of permanent magnet synchronous motor (PMSM) controllers. An optimized algorithm based on model predictive current control (MPCC) is proposed. By constructing a PHIL platform integrated with a digital twin model and combining multi-step prediction with feedback correction mechanisms, control delays are effectively compensated, and model mismatch is suppressed. Experimental results demonstrate that compared to PI control, the MPCC algorithm reduces the root mean square error of current tracking by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 2 \%}$</tex>, decreases the maximum overshoot of current and speed by 62 % and 79 %, respectively, and significantly enhances dynamic response. The study validates the engineering value of model predictive control in PHIL systems, providing an efficient solution for high-precision motor controller testing.