Intelligent Distributed Swarm Control for Large-Scale Multi-UAV Systems: A Hierarchical Learning Approach
Intelligent Distributed Swarm Control for Large-Scale Multi-UAV Systems: A Hierarchical Learning Approach
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DOI：
10.3390/electronics12010089
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发表时间：
2022-12
期刊：
影响因子：
2.9
通讯作者：
Shawon Dey;Hao Xu
中科院分区：
文献类型：
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作者：
Shawon Dey;Hao Xu
研究了大规模多智能体系统的分布式群体控制问题。与经典的多智能体系统不同，LS-MAS由于其智能体数量庞大，给控制设计带来了新的挑战。开发适当的控制来实现复杂的任务（如集体群集）可能会更加困难。为了解决这些挑战，一种新的混合博弈理论的分层学习算法。在混合博弈中，LS-MAS被表示为一个多群体、大规模的领导者-追随者系统。然后，一个合作的游戏是用来制定多组领导者的分布式群体控制，并利用Stackelberg游戏的领导者和他们的大规模的追随者有效地耦合。利用领导者和追随者之间的相互作用，平均场博弈被用来继续从领导者到追随者的集体行为顺利，而不增加计算复杂度或通信量。此外，设计了一种分层学习算法来学习多群主从系统的智能最优分布式群体控制。具体而言，多代理actor-critic算法的开发，以获得分布式最优群体控制的多组领导人第一。此外，一个行动者-评论家-质量的方法被设计来寻找大规模的追随者分散的群体控制。最后，一系列的数值模拟和一个李雅普诺夫稳定性证明的闭环系统进行证明所开发的计划的性能。
In this paper, a distributed swarm control problem is studied for large-scale multi-agent systems (LS-MASs). Different than classical multi-agent systems, an LS-MAS brings new challenges to control design due to its large number of agents. It might be more difficult for developing the appropriate control to achieve complicated missions such as collective swarming. To address these challenges, a novel mixed game theory is developed with a hierarchical learning algorithm. In the mixed game, the LS-MAS is represented as a multi-group, large-scale leader–follower system. Then, a cooperative game is used to formulate the distributed swarm control for multi-group leaders, and a Stackelberg game is utilized to couple the leaders and their large-scale followers effectively. Using the interaction between leaders and followers, the mean field game is used to continue the collective swarm behavior from leaders to followers smoothly without raising the computational complexity or communication traffic. Moreover, a hierarchical learning algorithm is designed to learn the intelligent optimal distributed swarm control for multi-group leader–follower systems. Specifically, a multi-agent actor–critic algorithm is developed for obtaining the distributed optimal swarm control for multi-group leaders first. Furthermore, an actor–critic–mass method is designed to find the decentralized swarm control for large-scale followers. Eventually, a series of numerical simulations and a Lyapunov stability proof of the closed-loop system are conducted to demonstrate the performance of the developed scheme.