Inversion-based adaptive learning control for a one-link flexible manipulator

Abstract

In this paper, an adaptive learning algorithm Is applied to one-link flexible manipulator. After each repetitive trial, Least-Squares method is used to estimate the system parameters. The output tracking error and the identified system model are used through stable inversion to find the feed forward input, together with the desired state trajectories, for the next trial. An adaptive backstepping based tracking controller is used in each trial to ensure the regulation of the desired state trajectories. Simulation results demonstrate that the proposed learning control scheme is very effective in tip trajectory tracking for a flexible link robotic manipulator.