A Note on Iterative Learning Control for Nonlinear Systems with Input Uncertainties

The problem of designing an iterative learning controller in the presence of input uncertainties is of great importance in practical implementations. This paper addresses this important issue for a simple scalar nonlinear dynamic system with general input uncertainties. A dual iterative learning loop is applied to systems to “learn” both unknown dynamics and static input uncertainties respectively and can ensure that the output of the system converges to the desired trajectory. Two analytic examples show that the proposed dual learning control scheme can work well under input uncertainties such as saturation and dead zone.