Decentralized Multi-Subgroup Formation Control With Connectivity Preservation and Collision Avoidance

Abstract

This paper proposes a formation control algorithm to create separated multiple formations for an undirected networked multi-agent system while preserving the network connectivity and avoiding collision among agents. Through the modified multi-consensus technique, the proposed algorithm can simultaneously divide a group of multiple agents into any arbitrary number of desired formations in a decentralized manner. Furthermore, the agents assigned to each formation group can be easily reallocated to other formation groups without network topological constraints as long as the entire network is initially connected; an operator can freely partition agents even if there is no spanning tree within each subgroup. Besides, the system can avoid collision without loosing the connectivity even during the transient period of formation by applying the existing potential function based on the network connectivity estimation. If the estimation is correct, the potential function not only guarantees the connectivity maintenance but also allows some extra edges to be broken if the network remains connected. Numerical simulations are performed to verify the feasibility and performance of the proposed multi-subgroup formation control.

Stochastic H∞ Robust Decentralized Tracking Control of Large-Scale Team Formation UAV Network System With Time-Varying Delay and Packet Dropout Under Interconnected Couplings and Wiener Fluctuations

“…3) Compared with the previous networked-based UAV formation control issues [39], [40], the other uncertain effects of external wind gust disturbance, intrinsic Wiener fluctuation, time-varying network-induced delay, packet dropout on team formation tracking performance are all simultaneously attenuated by the proposed decentralized H ∞ fuzzy controller for each UAV to efficiently achieve team formation of large-scale UAV NCS in this study. Moreover, the event-triggered mechanism is adopted for network bandwidth saving.…”

Multi-consensus formation control by artificial potential field based on velocity threshold

“…Additionally, all assumed states in this study are one-dimensional. The Kronecker ( Choi et al, 2020 ) product can be used if it is necessary to extend the methods of this study and the states such as position to multiple dimensions.…”

“…It is obvious that x t i ( )∈  represents the position of agent i and v t i ( )∈  represents the velocity of agent i. Additionally, all assumed states in this study are one-dimensional. The Kronecker (Choi et al, 2020) product can be used if it is necessary to extend the methods of this study and the states such as position to multiple dimensions.…”

UAV swarm containment control inspired by spatial interaction mechanism of wolf-pack foraging

“…尽管合围控制问题在多智能体协调控制领域已 得到广泛研究, 但任务环境的不确定性、合围过程中 可能出现的失效和中断等未知因素仍旧是其亟待解 决的主要难点. 传统合围控制方法按照不同约束条 件可分为基于连通拓扑的一致性控制 [7][8][9][10][11][12] 与人工势 场 [13][14][15][16][17] 两类方法. 一致性控制方法主要基于固定或 连通拓扑结构设计控制律, 以求使集群系统能够在 有限时间内收敛于相对目标实时位置的期望合围队 形, 常用于存在模型和环境不确定性的任务环境.…”