Farah Bouakrif
Asymptotic stability, forgetting factor, iterative learning control, Lyapunov theory
This paper addresses the problem of designing an iterative learning control with forgetting factor for trajectory tracking of rigid robot manipulators subject to external disturbances and performing repetitive tasks, without necessity to know neither the structure model of these robots nor the system parameters. Indeed, the advantage of this algorithm, it is not only applicable for robot manipulators with model uncertainty, but also for strictly unknown robots. It has been shown that the closed loop system (robot plus controller) is asymptotically stable, over the whole finite time interval, when the iteration number tends to infinity. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed controller scheme. Finally, simulation results on robot manipulator are provided to illustrate the effectiveness of the proposed controller.
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