Payload-adaptive iterative learning control for robotic manipulators

The main purpose of the iterative learning control (ILC) method is to reduce the trajectory tracking error caused by an inaccurate model of the robot's dynamics. It estimates the tracking error and applies a learning operator to the output control signals to correct them. Today's ILC researchers are suggesting strategies for increasing the ILC's overall performance and minimizing the number of iterations required. When a payload (or a different end effector) is attached to a robotic manipulator, the dynamics of the robot change. When a new payload is added, even the most accurately approximated model of the dynamics will be altered. This will necessitate changes to the dynamics estimates, which may be avoided if the ILC process is used to control the system. When robotic manipulators are considered, this study analyses how the payload affects the dynamics and proposes ways to improve the ILC process. The study looks at the dynamics of a SCARA-type robot. Its inertia matrix is determined by the payload attached to it. The results show that the ILC method can correct for the estimated inertia matrix inaccuracy caused by the changing payload but at the cost of more iterations. Without any additional data of the payload's properties, the suggested technique may adjust and fine-tune the learning operator. On a preset reference trajectory, the payload adaptation process is empirically tested. When the same payload is mounted, the acquired adaptation improvements are then utilized for another desired trajectory. A computer simulation is also used to validate the suggested method. The suggested method increases the overall performance of ILC for industrial robotic manipulators with a set of similar trajectories but different types of end effectors or payloads.