This paper investigates the event-triggered adaptive output-feedback control problem for a class of strictfeedback stochastic nonlinear systems, and a novel event-triggered adaptive neural network output-feedback control strategy is proposed. The radial basis function neural networks are utilized to approximate the unknown nonlinear functions.By introducing Nussbaum gain function and designing filter during the backstepping design procedure, the effect of unknown control direction is compensated. The boundness of the closed-loop stochastic nonlinear system is guaranteed by designing a relative threshold event-triggered mechanism. Finally, a numerical example is given to show the effectiveness of the proposed control strategy.%针对一类具有严格反馈结构且控制方向未知的随机非线性系统, 提出了基于事件触发机制的自适应神经网络 (Adaptive neural network, ANN) 输出反馈控制方法.利用径向基神经网络逼近系统中未知的非线性函数.通过引入Nussbaum增益函数并设计滤波器, 解决了系统控制方向未知的问题.通过设计具有相对阈值的事件触发机制, 保证了闭环随机非线性系统的有界性.最后给出数值仿真例子验证所提控制方法的有效性.
展开▼
