A computationally efficient algorithm of iterative learning control for discrete-time linear time-varying systems

Abstract

Iterative Learning Control (ILC) improves the tracking accuracy of systems that repetitively perform the same task. This paper considers model-based ILC for linear time-varying (LTV) systems. The applied feedforward iteratively minimises a quadratic norm of the feedforward update and the error in the next iteration as predicted by the model. The optimal feedforward update can be derived straightforwardly using a matrix description of the system dynamics. However, the implementation of the resulting matrix equation is demanding in terms of computation time and memory. In this paper it is shown that an efficient algorithm can be derived directly from the matrix equation using the associated state-equations. The ILC algorithm is applied to an industrial robot. The configuration dependent robot dynamics can be approximated as LTV for small tracking errors from the large-scale motion along the desired trajectory. It is shown that a substantial reduction of the tracking error at the robot’s tip can be realised by ILC using an LTV model of the robot dynamics and the same reduction cannot be accomplished using an LTI model that ignores the variation of the robot dynamics along the trajectory.