Adaptive iterative learning control via continuous sliding-mode technique with uncertainties

Abstract

In this paper, a new design of adaptive iterative learning control (ILC) for a class of uncertain nonlinear systems is proposed for the purpose of state tracking and improving convergence speed with both parametric and nonparametric uncertainties. The main feature of the design is that the controller signal is continuous due to the use of integral and employment of second-order sliding mode technique. Nonparametric uncertainties such as norm-bounded nonlinear uncertainties satisfying local Lipschitz condition can be effectively handled. In response to unknown bounded disturbances, a continuous sliding mode adaptive iterative learning control is more robust. By designing a suitable controller and composite energy function, the convergence of tracking error sequence within a small neighborhood of the origin is achieved in the iteration domain. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed ILC scheme.