Periodic learning disturbance observer based precision motion control in PMLSM motion systems considering long-term instability problem

Abstract

This paper presents a novel periodic learning disturbance observer(PLDOB) based controller that considers a long-term instability problem to achieve high precision position control in permanent magnet linear synchronous motor (PMLSM) motion systems under repetitive motion conditions. In the proposed scheme, an initial condition for PLDOB, which is the learning law in the proposed controller, is designed by a linear disturbance observer (DOB) due to an advantage that the learning law is designed directly from a point of a view of the disturbance, unlike ILC and RC. The use of the initial condition in the proposed scheme improves the learning speed to minimize tracking errors and guarantees the stability of the overall system at fast learning speed. In the proposed controller, no mathematical models are required to compensate for the dominant disturbances such as friction forces, force ripples, and model parametric errors in the PMLSM motion system, and the lumped disturbance is attenuated by PLDOB. The weaknesses induced by the use of Q-filter of DOB, such as the phase lag and limited frequency range for the estimated disturbance, are compensated, and it improves the transient performance and tracking performance. Also, theoretical analysis for the long-term instability problem is performed