Choi Yun Ho, Yoo Sung Jin
IEEE Trans Neural Netw Learn Syst. 2022 Jul;33(7):2965-2979. doi: 10.1109/TNNLS.2020.3047945. Epub 2022 Jul 6.
This article proposes a neural-network-based adaptive asynchronous event-triggered design strategy for the distributed consensus tracking of uncertain lower triangular nonlinear multi-agent systems under a directed network. Compared with the existing event-triggered recursive consensus tracking designs using multiple neural networks for each follower and continuous communications among followers, the primary contribution of this study is the development of an asynchronous event-triggered consensus tracking methodology based on a single-neural network for each follower under event-driven intermittent communications among followers. To this end, a distributed event-triggered estimator using neighbors' triggered output information is developed to estimate a leader signal. Subsequently, the estimated leader signal is used to design local trackers. Only a triggering law and a single-neural network are used to design the local tracking law of each follower, irrespective of unmatched unknown nonlinearities. The information of each follower and its neighbors is asynchronously and intermittently communicated through a directed network. Thus, the proposed asynchronous event-triggered tracking scheme can save communicational and computational resources. From the Lyapunov stability theorem, the stability of the entire closed-loop system is analyzed and the comparative simulation results demonstrate the effectiveness of the proposed control strategy.
本文提出了一种基于神经网络的自适应异步事件触发设计策略，用于有向网络下不确定下三角非线性多智能体系统的分布式一致性跟踪。与现有针对每个跟随者使用多个神经网络且跟随者之间进行连续通信的事件触发递归一致性跟踪设计相比，本研究的主要贡献在于开发了一种基于单个神经网络的异步事件触发一致性跟踪方法，该方法适用于跟随者之间事件驱动的间歇性通信情况。为此，开发了一种利用邻居触发输出信息的分布式事件触发估计器来估计领导者信号。随后，利用估计的领导者信号设计局部跟踪器。每个跟随者的局部跟踪律仅通过一个触发律和一个神经网络来设计，而无需考虑不匹配的未知非线性。每个跟随者及其邻居的信息通过有向网络进行异步和间歇性通信。因此，所提出的异步事件触发跟踪方案可以节省通信和计算资源。基于李雅普诺夫稳定性定理，分析了整个闭环系统的稳定性，比较仿真结果验证了所提控制策略的有效性。
