Iterative learning control with system relative degree

Abstract

In this thesis, the iterative learning control (ILC) problem for classes of nonlinear systems with well-defined relative degree is addressed and the methodologies such as continuous-time, sample-data and discrete-time ILCs are dealt with. The theoretic framework is presented via defining and analyzing the ILCs including highest-order differentiation, using lower-order differentiations or without using differentiation in open-loop configuration.