Prescribed-Time Robust Repetitive Learning Control for PMSM Servo Systems

This article proposes a prescribed-time robust repetitive learning control scheme for uncertain permanent magnet synchronous motor (PMSM) servo systems. An error-tracking approach is developed through constructing a desired error trajectory, such that the exact settling time of the error convergence can be achieved without using any switching mechanism in controller design. In order to achieve high-precision steady-state tracking accuracy, a fully saturated repetitive learning law is developed to reduce the residual periodic steady-state error and drive the tracking error to converge into a sufficiently small region around the origin, such that the rapid transient response and high-precision steady-state tracking accuracy of the PMSM servo system can be both guaranteed simultaneously. Comparative experiments are provided to verify the effectiveness of the proposed method.