Adaptive learning control for robot manipulators

Abstract

We propose an adaptive learning control for a robot manipulator which guarantees the tracking of a periodic reference signal in joint coordinates with an arbitrary precision. The control algorithm is not model based and it does not require acceleration measurements or persistently exciting reference signals: it adaptively reconstructs any Fourier series approximation of the periodic reference input at each joint from any initial condition in a compact set with guaranteed transient performance.