Event-triggered sliding mode control with adaptive neural networks for uncertain nonlinear systems

Abstract

In this paper, a robust non-singular fast terminal sliding mode control scheme with adaptive neural networks is presented for a class of nonlinear systems with unknown bounds of uncertainties. To reduce transmission and computation burden in resource-constrained networked systems, two kinds of event-triggering mechanisms are taken into consideration in the proposed adaptive sliding mode control scheme. The one, from the sensor to the controller, can guarantee finite-time convergence of system states into a predesigned band; and ensure that Zeno behavior does not occur. The other, from the controller to the actuator, can guarantee the asymptotically stability and Zeno behavior exclusion. Simulation results are given to verify the effectiveness and feasibility of the proposed schemes.