TY - GEN
T1 - Flocking Control Algorithm of Multiple Agents Based on Layered Pinning Control Theory
AU - Pang, Xiaolong
AU - Yang, Zhen
AU - Chai, Shiyuan
AU - Wang, Xingyu
AU - Zhou, De Yun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper starts from the concept of multi-Agent pinning control algorithm applied to UAV flocking control. Layered pinning control algorithm is proposed to optimize the tracking results for the problem of target tracking loss that may occur when the UAV clusters are large and there is external interference. According to the idea of breadth-first search, the speed and position feedback of its parent node is added to the control input of ordinary agents, and the speed matching term of the control input is improved by proposing a speed correction factor, which determines the degree of being affected by the neighborhood by judging the state of each other, so as to realize layer-by-layer transfer feedback and strengthen the tracking ability of the child nodes, which enables the whole network to avoid splitting and reach flocking. The simulation results show that the algorithm can strengthen the tracking ability of the agent and can track the target faster than the traditional pinning flocking control algorithm, which is suitable for large-scale UAV cluster flocking control.
AB - This paper starts from the concept of multi-Agent pinning control algorithm applied to UAV flocking control. Layered pinning control algorithm is proposed to optimize the tracking results for the problem of target tracking loss that may occur when the UAV clusters are large and there is external interference. According to the idea of breadth-first search, the speed and position feedback of its parent node is added to the control input of ordinary agents, and the speed matching term of the control input is improved by proposing a speed correction factor, which determines the degree of being affected by the neighborhood by judging the state of each other, so as to realize layer-by-layer transfer feedback and strengthen the tracking ability of the child nodes, which enables the whole network to avoid splitting and reach flocking. The simulation results show that the algorithm can strengthen the tracking ability of the agent and can track the target faster than the traditional pinning flocking control algorithm, which is suitable for large-scale UAV cluster flocking control.
KW - layered pinning control
KW - multi-agent
KW - neighborhood optimization
UR - http://www.scopus.com/inward/record.url?scp=85188533188&partnerID=8YFLogxK
U2 - 10.1109/ICRAE59816.2023.10458494
DO - 10.1109/ICRAE59816.2023.10458494
M3 - 会议稿件
AN - SCOPUS:85188533188
T3 - 2023 8th International Conference on Robotics and Automation Engineering, ICRAE 2023
SP - 211
EP - 215
BT - 2023 8th International Conference on Robotics and Automation Engineering, ICRAE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Robotics and Automation Engineering, ICRAE 2023
Y2 - 17 November 2023 through 19 November 2023
ER -