Iterative learning control for systems with nonparametric uncertainties under alignment condition

In this work, by incorporating the so-called alignment condition, a novel ILC scheme is proposed for a class of nonlinear systems with nonparametric local Lipschitz continuous (LLC) uncertainties to perform full state tracking tasks. A new Lyapunov-like energy function is adopted to facilitate the ILC design as well as property analysis, and thus achieve the asymptotical convergence of tracking error. More advantages of the proposed design approach lie in that it can handle the scenarios of state-dependent LLC input gain and high-order systems easily. In the end, an illustrative example is simulated to demonstrate the efficacy of the proposed ILC scheme.