Distributed Adaptive Flocking Control for Large-Scale Multiagent Systems
Distributed Adaptive Flocking Control for Large-Scale Multiagent Systems
复制标题
大规模多智能体系统的分布式自适应集群控制
DOI：
10.1109/tnnls.2023.3343666
复制
发表时间：
2024
影响因子：
10.4
通讯作者：
Xu, Hao
中科院分区：
文献类型：
--
作者：
Dey, Shawon;Xu, Hao
针对不确定环境下的大规模多智能体系统，提出了一种新的分布式群集控制方法。在不确定环境下处理大量群集智能体时，现有群集方法在求解基于偏微分方程的大系统最优群集控制问题时，会遇到通信复杂性和智能体交互指数增长所导致的“维数诅咒”问题.平均场博弈（MFG）方法通过将所有代理之间的相互作用转化为每个代理的相互作用来解决这个问题，其中每个代理的平均效应由其他代理的概率密度函数（PDF）表示。然而，仅仅依赖于pdf项来考虑其他代理的状态可能会导致低效的群集性能，这是由于缺乏涵盖群集中涉及的所有代理的熟练协调机制。为了克服这些困难并实现LS-MAS所需的聚集性能，将代理分解为有限数量的子组。每个子组包括一个领导者和追随者，和一个混合博弈论的开发来管理组间和组内的互动。该方法结合了一个合作的游戏，链接领导者从不同的群体，制定分布式群集控制，一个Stackelberg游戏，团队的领导者和追随者在同一组内，以扩展集体群集行为，和一个MFG的追随者，以解决LS-MAS的挑战。此外，为了实现分布式自适应群集使用的混合游戏结构，我们提出了一个分层的演员批评质量为基础的强化学习技术。这种方法采用了多演员批评的领导者和演员批评质量算法的追随者，使自适应群集控制在一个分布式的方式为大规模的代理。最后，通过数值仿真对比研究和李雅普诺夫分析，验证了该方法的有效性.
This article presents a novel distributed flocking control method for large-scale multiagent systems (LS-MASs) operating in uncertain environments. When dealing with a massive number of flocking agents in uncertain environments, existing flocking methods encounter the problem of communication complexity and “Curse of dimensionality” caused by the exponential growth of agent interactions while solving PDE-based optimal flocking control for large-scale systems. The mean field game (MFG) method addresses this issue by transforming interactions among all agents into the interaction of each individual agent with average effects represented by a probability density function (pdf) of other agents. However, relying solely on a pdf term to consider other agents’ states can result in inefficient flocking performance due to the absence of a proficient coordination mechanism encompassing all agents involved in flocking. To overcome these difficulties and achieve the desired flocking performance for LS-MASs, the agents are decomposed into a finite number of subgroups. Each subgroup comprises a leader and followers, and a hybrid game theory is developed to manage both inter-and intragroup interactions. The method incorporates a cooperative game that links leaders from different groups to formulate distributed flocking control, a Stackelberg game that teams up leaders and followers within the same group to extend collective flocking behavior, and an MFG for followers to address the challenges of LS-MASs. Furthermore, to achieve distributed adaptive flocking using the hybrid game structure, we propose a hierarchical actor–critic-mass-based reinforcement learning technique. This approach incorporates a multiactor–critic method for leaders and an actor–critic-mass algorithm for followers, enabling adaptive flocking control in a distributed manner for large-scale agents. Finally, numerical simulation including comparison study and Lyapunov analysis demonstrates the effectiveness of the developed method.