Global adaptive neural control for strict-feedback time-delay systems with predefined output accuracy

This paper focuses on the stabilization problem for a class of nonlinear strict-feedback systems with disturbances and time delay. By constructing some nonnegative functionals, a multiswitching-based adaptive backstepping neural state-feedback controller is designed. Compared with all the existing control methodologies for the uncertain time-delay systems, the outstanding merits of the proposed control scheme are presented as follows. First, the controller guarantees that all the signals in the closed-loop system remain globally bounded, meanwhile the system output achieves an accuracy that is given a priori according to the practical requirements. Second, in contrast to the classical adaptive backstepping neural control schemes, we analyze the convergence of the tracking error by using Barbalat's Lemma instead of Lyapunov stability theory. Third, the main technical novelty is to construct three new nth-order continuously differentiable functions which are used to design the actual controller, the virtual control variables and the adaptive laws. Two simulation examples are provided to illustrate the effectiveness and advantage of the proposed control scheme.