Abstract
This paper presents a robust adaptive repetitive control(RARC) method for trajectory tracking of uncertain robotic manipulators. Repetitive control is applied for periodic trajectory tracking and a σ modification is introduced in the periodic learning laws to guarantee the robustness of the system. All the signals in the closed loop are proved to be bounded. An open-loop learning algorithm with switching σ modification is designed to achieve asymptotic convergence of the tracking errors when the disturbances disappear. The simulation is made to show the effectiveness of the algorithms.
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