Iterative learning controller designs with finite-time convergence performance

This paper presents an iterative learning control scheme for a class of uncertain systems in the presence of initial state errors. The terminal attractor is adopted in the control design. An iterative learning algorithm is proposed to deal with time-varying parametric uncertainties, while a periodic learning algorithm is given to cope with time-invariant ones. It is shown through the theoretical analysis and numerical simulation that system states completely follow the desired trajectories after the pre-specified time, and all the signals in the closed-loop are guaranteed to be bounded.