Adaptive Iterative Learning Control for Robot Manipulators Without Using Velocity Signals

This paper proposes an output based adaptive iterative learning control (OBAILC) scheme for robotic systems. The idea of using OBAILC is to improve the tracking performance iteratively with relatively smaller values of observer-controller gains by assuming that the system tracks the same task iteratively. The design combines proportional-derivative controller with an adaptive term that iteratively updates uncertain parameters where unknown velocity signals are estimated by the output of the linear observer. The Lyapunov-based online switching mechanism is employed to ensure monotonic convergence of the tracking errors with respect to iteration number. The proposed scheme is evaluated on a 2-DOF robot manipulator to demonstrate the theoretical development of this paper.