Feedforward Enhancement through Iterative Learning Control for Robotic Manipulator.

This work presents an iterative learning control (ILC) algorithm to enhance the feedforward control (FFC) for robotic manipulators. The proposed ILC algorithm enables the cooperation between the ILC, inverse dynamics, and a PD feedback control (FBC) module. The entire control scheme is elaborated to guarantee the control accuracy of the first implementation; to improve the control performance of the manipulator progressively with successive iterations; and to compensate both repetitive and non-repetitive disturbances, as well as various uncertainties. The convergence of the proposed ILC algorithm is analysed using a well established Lyapunov-like composite energy function (CEF). A trajectory tracking test is carried out by a seven-degree-of-freedom (7-DoF) robotic manipulator to demonstrate the effectiveness and efficiency of the proposed control scheme. By implementing the ILC algorithm, the maximum tracking error and its percentage respect to the motion range are improved from 5.78 degrees to 1.09 degrees, and 21.09% to 3.99%, respectively, within three iterations.