Adaptive Self-Tuning Control of Robot Manipulators with Periodic Disturbance Estimation

This paper addresses the problem of position tracking control of robot manipulators in the presence of parametric uncertainty and additive periodic disturbances. Specifically, a self tuning, Lyapunov-based adaptive controller with desired dynamics compensation term and a disturbance estimator has been designed to ensure that the link position tracking error converges to zero asymptotically, despite the partially linearly parametrizable robot dynamics. Extensive experimental results are provided to illustrate the viability and performance of the proposed controller.