Iterative Learning Control for a Class of Partial State Measurable Systems

Abstract

In this paper, the tracking problem for a class of nonparametric systems is discussed, in which only partial system state can be measurable. After implementing reasonable coordinate transformations, an adaptive iterative learning control scheme is developed with the partial measurable system state information used. Iterative learning control and Robust control are together used to deal with non-parametric uncertainties under alignment condition. As the iteration number increases, the system output can follow the desired trajectory over the full period, and all signal are guaranteed to be bounded. A simulation example is given to verify the effectiveness of the proposed iterative learning control scheme.