Decentralized Robust Adaptive Iterative Learning Control of Robot Manipulators

Abstract

In this paper, a decentralized robust adaptive iterative learning control scheme for trajectory tracking of robot manipulators is developed. In this scheme, each joint is considered as a subsystem and controlled independently. The interactions from other subsystems are treated as uncertainties with unknown nonlinear bounds. It is shown that by using the proposed decentralized robust adaptive iterative learning controller, the states of the subsystems can track the desired trajectories. The asymptotic convergence of the tracking error is proved in the paper. The effectiveness of scheme is demonstrated through a robot manipulator simulation.