Comparative Study of Sphere Decoding Algorithm and FCS-MPC for PMSMs in Aircraft Application

Permanent Magnet Synchronous Motors (PMSMs) require advanced control strategies to meet the high-performance demands of aerospace applications. Surface-mounted PMSMs (SPMSMs) have gained popularity in aeronautical systems due to their superior power density, efficiency, and reliability. In an SPMSM, the Iq current is directly proportional to torque, making Total Harmonic Distortion (THD) reduction essential for minimizing torque ripple and ensuring smooth operation. In this study, we propose a long prediction horizon Model Predictive Control (MPC) framework for PMSMs. Initial simulations using a one-norm cost function resulted in instability in switching frequency control, particularly due to the inherent limitations imposed by the sampling interval when no control effort was applied. To mitigate this, we reformulated the MPC framework using a two-norm cost function within a Sphere Decoding Algorithm (SDA), which at high sampling intervals (>40μs) resulted in an undershoot in the direct-quadrature axis. Extensive simulations were conducted over a range of sampling intervals (1–80μs), revealing that while a 10μs interval achieved the lowest THD, it also led to an increased switching frequency. To address this trade-off, a weighting factor tuning approach was employed, effectively reducing switching frequency while maintaining acceptable THD levels. Further investigations analyzed the effects of three-step and five-step prediction horizons, as well as parameter mismatches in the long prediction formulation, providing critical insights into controller robustness. These findings underscore the importance of norm selection, sampling interval optimization, and weighting factor adjustments in balancing THD reduction and switching frequency. The proposed approach enhances system efficiency, reliability, and overall performance, offering significant implications for high-performance aerospace PMSM applications.