On the iterative learning control of sampled-data systems

In this chapter 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 whose input-output coupling matrix may be singular at some time instant or for some states. 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.