On iterative learning from different tracking tasks in the presence of time-varying uncertainties

In this paper, we introduce a new iterative learning control (ILC) method, which enables learning from different tracking control tasks. The proposed method overcomes the limitation of traditional ILC in that, the target trajectories of any two consecutive iterations can be completely different. For nonlinear systems with time-varying and time-invariant parametric uncertainties, the new learning method works effectively to nullify the tracking error. To facilitate the learning control system design and analysis, in the paper we use a composite energy function (CEF) index, which consists of a positive scalar function and L/sup 2/ norm of the function approximation error.