IEEE Trans Neural Netw Learn Syst. 2023 Jun;34(6):2742-2752. doi: 10.1109/TNNLS.2021.3107623. Epub 2023 Jun 1.
This article systematically addresses the distributed event-triggered containment control issues for multiagent systems subjected to unknown nonlinearities and external disturbances over a directed communication topology. Novel composite distributed adaptive neural network (NN) event-triggering conditions and event-triggered controller are raised meanwhile. Furthermore, the designed event-triggered controller is updated in an aperiodic way at the moment of event sampling, which saves the computation, resources, and transmission load. On the basis of the NN-based adaptive control techniques and event-triggered control strategies, the uniform ultimate bounded containment control can be achieved. In addition, the Zeno behavior is proven to be excluded. Simulation is presented to testify the effectiveness and advantages of the presented distributed containment control scheme.
本文针对具有未知非线性和外部干扰的多智能体系统，在有向通信拓扑结构下，系统地解决分布式事件触发的牵制控制问题。同时提出了新颖的复合分布式自适应神经网络（NN）事件触发条件和事件触发控制器。此外，所设计的事件触发控制器在事件采样时刻以非周期性方式进行更新，从而节省了计算、资源和传输负载。基于基于 NN 的自适应控制技术和事件触发控制策略，可以实现一致的最终有界牵制控制。此外，证明了可以排除零和行为。通过仿真验证了所提出的分布式牵制控制方案的有效性和优势。
