Iterative learning control for robotic contouring

A dynamic adaptive iterative learning control method applied to contouring control is developed to reduce the contour tracking error for a planar robotic system with parametric uncertainty. The proposed control scheme takes advantages of the self-tuning law and adaptive iterative learning control, which consists of a feedback controller and an adaptive learning term. The self-tuning method is applied to feedback control in the iteration domain, and a hybrid adaptive updating law is used to estimate the control parameter in the adaptive learning term in both the time domain and the iteration domain. The dynamic control algorithm not only compensates unknown bounded parametric uncertainties and disturbances in the time and iterative domains but also eliminates actuator chattering. The closed-loop control system can reduce the contouring error by improving the coordination between axes. Properties of the learning control algorithm are studied analytically. A numerical example is given to demonstrate the effectiveness of the proposed approach.