Prescribed performance adaptive neural output control for a class of switched nonstrict-feedback nonlinear time-delay systems: State-dependent switching law approach

This paper investigates the tracking problem for a class of uncertain switched nonlinear delayed systems with nonstrict-feedback form. To address this problem, by introducing a new common Lyapunov function (CLF), an adaptive neural network dynamic surface control is proposed. The state-dependent switching rule is designed to orchestrate which subsystem is active at each time instance. In order to compensate unknown delay terms, an appropriate Lyapunov-Krasovskii functional is considered in the constructing of the CLF. In addition, a novel switched neural network-based observer is constructed to estimate system states through the output signal. To maintain the tracking error performance within a predefined bound, a prescribed performance bound approach is employed. It is proved that by the proposed output-feedback control, all the signals of the closed-loop system are bounded under the switching law. Moreover, the transient and steady-state tracking performance is guaranteed by the prescribed performance bound. Finally, the effectiveness of the proposed method is illustrated by two numerical and practical examples.