Sampled-data iterative learning control for a class of nonlinear systems

In this paper, a sampled-data iterative learning control (ILC) method is proposed for a class of nonlinear continuous-time systems with higher-order relative degree. The learning control does not require differentiation of tracking error. As the sampling period is set to be small enough, a sufficient condition is derived to guarantee the convergence of the learning process. This method can be applied to a more general class of nonlinear continuous-time systems that the most existing ILC methods fail to work.