In this paper, an event-triggered adaptive neural output feedback control scheme is developed for a class of uncertain discrete-time strict-feedback nonlinear systems subject to immeasurable system states and network resource limitation. An i-step ahead predictor is synthesized to obtain the future signal of the reference orbit. By combining the neural observer and the variable substitution technology, an event-triggered adaptive neural control scheme is developed, thereby estimating the immeasurable system states and avoiding the n-step delays of the existing controller. To promote the transient system performance, an improved triggering condition is designed to increase the number of triggering events in the transient process. The stability analysis of the closed-loop system is divided into two parts to deal with the challenge from the simultaneous presence of state estimations, unknown system dynamics, and aperiodical controller weight updating laws. The proposed scheme achieves the state estimation, guarantees the output tracking performance with the improved transient control performance, and reduces the communication resource. Simulation studies on a numerical example and a networked robot manipulator are, respectively, implemented to show the validity of the proposed scheme.

中文翻译：

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基于观测器的自适应神经输出反馈事件触发控制离散时间非线性系统的变量代入

本文针对一类具有不可测系统状态和网络资源限制的不确定离散时间严格反馈非线性系统，开发了一种事件触发的自适应神经输出反馈控制方案。一个我-步骤提前预测器被合成，以获得基准轨道的未来信号。通过结合神经观测器和变量代换技术，开发了一种事件触发的自适应神经控制方案，从而估计不可测系统状态并避免n- 现有控制器的步骤延迟。为了提升瞬态系统性能，设计了一种改进的触发条件，以增加瞬态过程中触发事件的数量。闭环系统的稳定性分析分为两部分，以应对同时存在的状态估计、未知系统动力学和非周期性控制器权重更新规律的挑战。所提出的方案实现了状态估计，以改进的瞬态控制性能保证了输出跟踪性能，并减少了通信资源。分别对数值示例和网络机器人操纵器进行了仿真研究，以证明所提出方案的有效性。