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.

针对一类不确定性未知的非线性系统，提出了一种具有自适应神经网络的鲁棒非奇异快速终端滑模控制方案。为了减少资源受限的网络系统的传输和计算负担，在提出的自适应滑模控制方案中考虑了两种事件触发机制。从传感器到控制器，可以保证系统状态在有限时间内收敛到预先设计的频段；并确保不会发生Zeno行为。从控制器到执行器，另一个可以保证渐近稳定性和芝诺行为排除。仿真结果验证了所提方案的有效性和可行性。