Trajectory Tracking Control of Robotic Manipulator via Variable Gain Iterative Learning Algorithm

Abstract

For the trajectory tracking of a class of strong coupling and uncertain manipulator system, a PD-type iterative learning control law with exponential variable gain is put forward. It is shown that under the input state disturbance and the output state disturbance, the proposed iterative learning control law can make the tracking errors converge. Simulation on the two-degree manipulators show the effectiveness of the algorithm.