Zhou Haodong, Tong Shaocheng
IEEE Trans Cybern. 2023 Nov;53(11):7406-7416. doi: 10.1109/TCYB.2023.3249154. Epub 2023 Oct 17.
This article investigates the adaptive neural network (NN) event-triggered containment control problem for a class of nonlinear multiagent systems (MASs). Since the considered nonlinear MASs contain unknown nonlinear dynamics, immeasurable states, and quantized input signals, the NNs are adopted to model unknown agents, and an NN state observer is established by using the intermittent output signal. Subsequently, a novel event-triggered mechanism consisting of both the sensor-to-controller and controller-to-actuator channels are established. By decomposing quantized input signals into the sum of two bounded nonlinear functions and based on the adaptive backstepping control and first-order filter design theories, an adaptive NN event-triggered output-feedback containment control scheme is formulated. It is proved that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) and the followers are within a convex hull formed by the leaders. Finally, a simulation example is given to validate the effectiveness of the presented NN containment control scheme.
本文研究了一类非线性多智能体系统（MASs）的自适应神经网络（NN）事件触发包容控制问题。由于所考虑的非线性MASs包含未知非线性动力学、不可测量状态和量化输入信号，采用神经网络对未知智能体进行建模，并利用间歇输出信号建立了神经网络状态观测器。随后，建立了一种由传感器到控制器和控制器到执行器通道组成的新型事件触发机制。通过将量化输入信号分解为两个有界非线性函数之和，并基于自适应反步控制和一阶滤波器设计理论，制定了一种自适应神经网络事件触发输出反馈包容控制方案。证明了受控系统是半全局一致最终有界（SGUUB）的，并且跟随者位于由领导者形成的凸包内。最后，给出了一个仿真例子来验证所提出的神经网络包容控制方案的有效性。
