The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite‐time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single‐integrator model and double‐integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite‐time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite‐time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.

中文翻译：

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具有有限时间群簇的分布式多智能体系统的优化控制

受自然现象的群体行为启发，多个智能体的集群已广泛应用于无人机（UAV）和机器人系统等工程领域。然而，当智能代理参与协同工作时，系统的性能（如响应时间、网络吞吐量和资源利用率）可能会受到很大影响。因此，在保证智能系统最优性能的同时，实现分布式协作。在本文中，我们研究了具有有限时间群聚集运动的分布式多智能体系统 (MAS) 的最优控制问题。具体而言，我们提出了两种具有单积分器模型和双积分器模型的 MAS 的最优群聚算法。然后，我们利用现代控制理论和有限时间收敛理论研究分布式 MAS 的群共识，其中提出的最优控制算法可以驱动 MAS 在有限时间内实现群收敛，同时最小化智能系统的性能指标。最后，实验仿真表明，在最优控制算法的作用下，MASs可以保持最小能量函数，而智能代理可以遵循最优轨迹在有限时间内实现群聚。