Sampled-data iterative learning control for SISO nonlinear systems with arbitrary relative degree

This paper presents a sampled-data iterative learning control method for SISO nonlinear systems with arbitrary relative degree, which does not require any differential approximation of the tracking error. Under certain conditions, this method guarantees the convergence of the system output to a desired trajectory at each sampling instant. It is shown to be applicable to a more general class of nonlinear systems that most of the existing iterative learning control methods fail to work