TY - JOUR
T1 - Distributed event-triggered formation control of UGV-UAV heterogeneous multi-agent systems for ground-air cooperation
AU - XIONG, Hao
AU - DENG, Hongbin
AU - LIU, Chaoyang
AU - WU, Junqi
N1 - Publisher Copyright:
© 2024
PY - 2024/12
Y1 - 2024/12
N2 - Within the context of ground-air cooperation, the distributed formation trajectory tracking control problems for the Heterogeneous Multi-Agent Systems (HMASs) is studied. First, considering external disturbances and model uncertainties, a graph theory-based formation control protocol is designed for the HMASs consisting of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). Subsequently, a formation trajectory tracking control strategy employing adaptive Fractional-Order Sliding Mode Control (FOSMC) method is developed, and a Feedback Multilayer Fuzzy Neural Network (FMFNN) is introduced to estimate the lumped uncertainties. This approach empowers HMASs to adaptively follow the expected trajectory and adopt the designated formation configuration, even in the presence of various uncertainties. Additionally, an event-triggered mechanism is incorporated into the controller to reduce the update frequency of the controller and minimize the communication exchange among the agents, and the absence of Zeno behavior is rigorously demonstrated by an integral inequality analysis. Finally, to confirm the effectiveness of the proposed formation control protocol, some numerical simulations are presented.
AB - Within the context of ground-air cooperation, the distributed formation trajectory tracking control problems for the Heterogeneous Multi-Agent Systems (HMASs) is studied. First, considering external disturbances and model uncertainties, a graph theory-based formation control protocol is designed for the HMASs consisting of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). Subsequently, a formation trajectory tracking control strategy employing adaptive Fractional-Order Sliding Mode Control (FOSMC) method is developed, and a Feedback Multilayer Fuzzy Neural Network (FMFNN) is introduced to estimate the lumped uncertainties. This approach empowers HMASs to adaptively follow the expected trajectory and adopt the designated formation configuration, even in the presence of various uncertainties. Additionally, an event-triggered mechanism is incorporated into the controller to reduce the update frequency of the controller and minimize the communication exchange among the agents, and the absence of Zeno behavior is rigorously demonstrated by an integral inequality analysis. Finally, to confirm the effectiveness of the proposed formation control protocol, some numerical simulations are presented.
KW - Distributed formation control
KW - Event-triggered control
KW - Feedback multilayer fuzzy neural network
KW - Fractional-order sliding mode control
KW - Heterogeneous multi-agent systems
UR - http://www.scopus.com/inward/record.url?scp=85208467210&partnerID=8YFLogxK
U2 - 10.1016/j.cja.2024.05.035
DO - 10.1016/j.cja.2024.05.035
M3 - Article
AN - SCOPUS:85208467210
SN - 1000-9361
VL - 37
SP - 458
EP - 483
JO - Chinese Journal of Aeronautics
JF - Chinese Journal of Aeronautics
IS - 12
ER -