Nonparametric statistical learning control of robot manipulators for trajectory or contour tracking

Abstract

This paper presents a method of precision tracking control for industrial robot manipulators. For robotic laser and plasma cutting tasks, the required tracking performance is much more demanding than that for material handling, spot welding, and machine tending tasks. Challenges in control come from the nonlinear coupled multi-body dynamics of robot manipulators, as well as the transmission error in the geared joints. The proposed method features data-driven iterative compensation of torque and motor reference. Motor side tracking and transmission error are handled by separate learning modules in a two-part compensation structure. Depending on the specific setup of end-effector sensing, the method can utilize either timed trajectory measurement or untimed two-dimensional contour inspection. Nonparametric statistical learning is used for the compensation. Considerations on incorporating analytical models and selecting data subsets for more efficient learning are discussed. The method is validated using a six-axis industrial robot. HighlightsA control method of robot manipulators for precision trajectory or contour tracking is presented.Nonparametric statistical learning is used for data-driven compensation of both torque and motor reference.A two-part compensation structure is adopted to handle motor side tracking and transmission error.The compensation can be done using either timed trajectory measurement or untimed contour inspection.