Friction Compensation Method with Iterative Learning Based Bang-Bang Compensator in PMLSM System

Nonlinear friction force is the predominant force disturbance in permanent-magnet linear synchronous motor (PMLSM) with guide rails, and it would worsen position control precision, especially during motion reversals (velocity zero-crossing). To minimize the effect of nonlinear friction on position accuracy in the reciprocating motion for PMLSM, a compensation scheme which consists of a iterative learning-based bang-bang compensator (ILBBC) and a generalized proportional-integral observer (GPIO) is presented in this paper. ILBBC is utilized to compensate for abruptly changing Coulomb friction force in velocity zero-crossing, and GPIO is added to enhance compensation performance for vicious friction force. To effectively merge ILBBC and GPIO, three-stage compensation scheme is proposed, thus, the combination of ILBBC and GPIO is capable of compensating for nonlinear friction in both velocity zero-crossing and non-zero regions. Design and stability of ILBBC and GPIO are theoretically analyzed. Finally, comparative experiments which include no compensation, GPIO-based compensation, and ILBBC + GPIO compensation are implemented under two kinds of sinusoidal position trajectories to demonstrate the effectiveness of the proposed compensation scheme.