Adaptive Iterative Learning Control for Robotic Manipulators

Abstract

A refined control scheme is presented to optimise the trajectory-tracking performance of robotic manipulators. This strategy integrates a linear feedback controller and a feedforward learning controller. The former mitigates unknown disturbances and desensitises the system to unidentified parameters, while the latter enhances tracking efficiency by incorporating past tracking errors. Traditionally, a fixed learning gain is used in the learning law to update the control input. However, we modify the learning law in this study by applying an adaptive learning gain. Our simulations on a robotic manipulator demonstrate that the adaptive ILC algorithm surpasses the classical ILC algorithm concerning convergence speed. These findings highlight the advantages of our approach, showcasing its extensive applicability in trajectory tracking.