Advanced GTCF-LARC Contouring Motion Controller Design for an Industrial X–Y Linear Motor Stage With Experimental Investigation

Abstract

In this paper, a global task coordinate frame (GTCF)-based learning adaptive robust contouring controller is proposed for an industrial X–Y linear-motor-driven stage to achieve not only good parametric adaptation ability and disturbance robustness, but also excellent contouring accuracy even under high-speed large-curvature contouring tasks. Specifically, the contouring controller employs GTCF to guarantee the multiaxes motion coordination. After transforming the system dynamics of the X–Y linear-motor-driven stage into the GTCF, a learning adaptive robust control (LARC) scheme is developed to deal with the strongly coupled dynamics under parametric uncertainty and uncertain disturbances. During the LARC, adaptive model compensation term, robust feedback term, and iterative learning term are organically integrated in a serial structure. The controller design process with the stability analysis is presented, while the essence of the practical achievable performance is also introduced for the nature of the GTCF-LARC. Comparative experiments are carried out on an industrial linear-motor-driven stage with different cases. The results consistently verify that the proposed GTCF-LARC contouring controller can simultaneously meet the industrial requirements of excellent transient/steady-state contouring accuracy, parametric adaptation ability, external disturbance robustness, and large-curvature high-speed contouring tasks. The proposed GTCF-LARC scheme actually provides a practical high-performance-oriented contouring control framework, and could be extended to other multiaxes applications.