The sampled-data iterative learning control for nonlinear systems

In this paper a sampled-data iterative learning controller is proposed for a class of nonlinear continuous-time systems with uncertainties. The learning algorithm is constructed without any differentiation of the learning error and can be applied to a more general class of nonlinear systems with zero or singular input-output coupling matrix. A rigorous proof via a discrete approach is given to study the convergence and robustness. Under a sufficient condition on the learning operator, the uniform boundedness between the plant output and the desired output can be shown at each sampling instant if the sampling period is small enough.