Aperiodic Speed Tracking With Predefined Performance for PMSM Systems Using Adaptive Spatial Repetitive Learning Control

This article proposes an adaptive spatial repetitive learning control scheme to achieve high-precision tracking of aperiodic speed trajectories with predefined performance for permanent magnet synchronous motor (PMSM) systems. The position-related periodic uncertainty induced by the angular position is accurately compensated through constructing a fully saturated spatial repetitive learning law, and thus the high-precision tracking for aperiodic desired trajectories can be achieved. For guaranteeing the predefined performance of the speed tracking error convergence, a predefined-position control law is designed to drive the tracking error to converge into a small region near the origin within the predefined position. The experimental studies on a three-phase PMSM setup are provided to corroborate the theoretical results. It is shown that the proposed scheme can enable the predefined-position convergence of the speed tracking error, and the steady-state accuracy is further improved under the effect of spatial repetitive learning law, so as to improve the speed tracking performance of the PMSM system effectively.