Adaptive Iterative Learning Constrained Control for Linear-Motor-Driven Gantry Stage

In this article, an adaptive iterative learning constrained control method of a linear-motor-driven gantry stage is proposed, which synthetically solves the problems of practical state and input constraints of the gantry stage and avoids the difficulty of accurate modeling. Through iterative learning and backstepping collaborative design, the proposed method yields the system stability without a priori knowledge of the system dynamic model and parameters. By using the barrier composite energy function, the controller can effectively regulate the plant to operate stably even when the states are constrained. To address the instability problem caused by a constrained input, the input term is specially constructed to remove the negative effects of constraints. Experiments on a linear-motor-driven gantry stage attest to the method efficacy.