Iterative Learning Control Scheme for Manipulators

This paper presents the iterative learning control for the industrial robot manipulator that performs repeated tasks. Motivated by human learning, the basic idea of iterative learning control is to use information from previous execution of a trial in order to improve performance from trial to trial. This is an advantage, when accurate model of the systems is not available. In this paper different aspects of ILC including the design schemes and control algorithms are covered. The Learning Control scheme comprising of two types of control laws: A linear feedback law and a unique feed-forward control law. In the feedback loop, the fixed gain PD-controller provides stability of the system and keeps its state errors within uniform bounds. In the feed-forward path, a learning control rule/strategy is exploited to track the entire span of a reference input over a sequence of iterations. Algorithms are verified through simulation results on two DOF robot manipulator including actuator dynamics.