Discrete-Time Attitude Control for Spacecraft Using Iterative Learning

Abstract

This study aims at the attitude control problem of spacecraft with periodic disturbances and model uncertainty by using anticipatory Iterative Learning Control (ILC), a combination of feedback control and ILC in a discrete-time system. Using a sampling point a little forward in one previous cycle, the convergence accuracy to the target value is improved step by step by the anticipatory ILC scheme, and its effectiveness against periodic disturbances is verified. In addition, we compare the accuracy to the target value by changing the sampling point selected in one previous cycle.