Adaptive iterative learning control of nonlinearly parameterised strict feedback systems with input saturation

In this paper, a new adaptive iterative learning control scheme is proposed to deal with nonlinearly parameterised strict feedback systems under alignment condition in the presence of input saturation constraint. The learning controller is designed by using the command filtered adaptive backstepping design procedure. The nonlinearly connected parameters are separated from the local Lipschitz continuous nonlinear functions and then learning laws are designed in iteration domain. To overcome the problem of input saturation, an auxiliary system is constructed with the same order as that of the systems under consideration. It is proved that the proposed control scheme can guarantee that all signals of the resulting closed-loop system remain bounded, and the tracking error converges to zero as the iteration number goes to infinity. A simulation example is included to illustrate the effectiveness of the proposed scheme.