Higher-order Adaptive Iterative Control for Uncertain Robot Manipulators

This paper presents higher-order adaptive iterative learning control for trajectory tracking of uncertain robot manipulators. The proposed control schemes have been given rigorous proof of convergence under some assumptions. The schemes are based upon the use of a proportional-derivative (PD) feedback structure, for which an iterative term is added to cope with the unknown parameters and disturbances. Higher-order adaptive iterative learning control has potential to give a better convergence performance than the first-order scheme algorithms ,because of using past system control information from more than one past iterative cycle. The effectiveness of the proposed method is shown through numerical simulation results.