Variable Gain Adaptive Iterative Learning Control for Industrial Robots

For industrial robots performing repetitive motion, a variable gain adaptive iterative learning control algorithm is proposed to ensure the position and velocity tracking accuracy of the robot. Firstly, exponential variable gain is added in the PD feedback part to accelerate the convergence of the algorithm. Next, a generalized error function is designed in the parameter adaptive part to further reduce the trajectory tracking error and enhance the stability of the system. The convergence of the proposed variable gain adaptive iterative learning control algorithm is theoretically proved with Lyapunov function. Finally, simulation results show that the control algorithm can effectively reduce the trajectory tracking error of the robot and accelerate the convergence of the algorithm.