Robust PD-type Iterative Learning Control design for trajectory tracking of a Pelican robot

Abstract

This work focuses on the application of the Proportional-Derivative (PD)-type Iterative Learning Control (ILC) to the two-degree-of-freedom (2DOF) Pelican robot manipulator, particularly in the context of cyclic tasks, aiming to address the persistent challenges of trajectory tracking and steady-state error. To ensure that the system remains stable and that errors diminish over time, the control law is updated iteratively. The simulations carried out on the Pelican robot clearly demonstrate the effectiveness and robustness of this method. By taking past tracking errors into account, the control is gradually refined. This learning process not only enhances trajectory tracking precision but also allows the system to adapt its behavior in response to load changes and variations in operating conditions.