Adaptive Iterative Learning Control for a Class of Nonlinear Systems with Unknown Control Directions

An adaptive iterative learning control(ILC) approach is proposed for a class of uncertain nonlinear systems without prior knowledge about system control directions. The Nussbaum-type gain and the positive definite discrete matrix kernel are proposed for dealing with selection of the unknown control gain and learning of the repeatable uncertainties, respectively. Asymptotic convergence for a trajectory tracking within a finite time interval is achieved through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.