Event-triggered adaptive neural control of fractional-order nonlinear systems with full-state constraints

Abstract

In this article, we present an event-triggered scheme for fractional order nonlinear systems under full-state constraints. The neural networks are employed to approximate the continuous nonlinear unknown functions in the system. Moreover, a new adaptive event-triggered strategy is designed under the unified framework of backstepping control method, which can largely reduce the amount of communications. Since the state constraints are frequently emerged in the control procedure, the barrier Lyapunov functions is used to avoid the violation of state constraints. The stability of the closed-loop system is ensured through fractional Lyapunov stability analysis. Finally, the effectiveness of the proposed scheme is verified by simulation examples.