Adaptive Iterative Learning Control with Initial State Learning for Nonlinear Parameterized-Systems

In this paper, for a class of non-linearly parameterized systems with time-varying parameters, an adaptive iterative learning control method based on initial state learning is proposed. by using the parameter separation and the initial state learning, a novel adaptive control strategy is designed to ensure the tracking error converge to zero in the mean-square sense on a finite time-interval. a sufficient condition for the convergence is also given by constructing a Lyapunov function. the approach can be applied to the nonlinear systems with time-varying parameters and a certain degree of orientation bias in the initial condition. Based on the convergence condition, the learning gain of initial learning principle, the gain of input learning principle and the gain of adaptive principle can be determined. the simulation example shows that the proposed learning algorithms are effective.