On the iterative learning control theory for robotic manipulators

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Abstract
An iterative learning technique is applied to robot manipulators, using an inherently nonlinear analysis of the learning procedure. In particularly, a 'high-gain feedback' point of view is utilized to prove the possibility of setting up uniform upper bounds to the trajectory errors occurring at each trial. The subsequent analysis of convergence shows that apart from minor conditions, the existence of a finite (but not necessarily narrow) bound on the trajectory deviations can substantially suffice to guarantee the zeroing of the errors after a sufficient number of trials. This in turn leaves open the possibility of obtained the exact tracking of the desired motion, even in the presence of moderate values assigned to the feedback gains.

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