Adaptive learning control of robotic systems with model uncertainties

Abstract

An adaptive-learning (AL) control scheme is developed for control of robotic systems with model uncertainties. When robots perform repetitive tasks, their operations are decomposed into two modes: the single operational mode and the repetitive operational mode. In the single operational mode, the control is a learning based adaptive control where the parameters of the system are updated by using the information of the previous operation. In the repetitive operational mode, the control is a model-based iterative learning control. The advantage of the AL scheme lies in the ability to improve the transient performance at a high rate of learning convergence as robots repeat their operations. Experimental and simulation results ascertain the effectiveness of the AL scheme in controlling a single and multiple robots with model uncertainties.