Data-driven time-varying formation-containment control for a heterogeneous air-ground vehicle team subject to active leaders and switching topologies

被引：10

作者：

Cheng, Ming ^{[1
,2
]}

Liu, Hao ^{[2
,3
]}

Wen, Guanghui ^{[4
]}

Lu, Jinhu ^{[3
]}

Lewis, Frank L. ^{[5
]}

机构：

[1] Beihang Univ, Sch Astronaut, Beijing 100191, Peoples R China

[2] Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China

[3] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China

[4] Southeast Univ, Sch Math, Dept Syst Sci, Nanjing 210096, Peoples R China

[5] Univ Texas Arlington, Res Inst, Ft Worth, TX 76118 USA

来源：

AUTOMATICA | 2023年 / 153卷

基金：

中国国家自然科学基金;

关键词：

Formation-containment control; Switching topology; Air-ground coordination; Reinforcement learning; Quadrotors; FORMATION TRACKING CONTROL; SYSTEMS;

D O I：

10.1016/j.automatica.2023.111029

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The optimal formation-containment control problem for a team of heterogeneous unmanned airground vehicles (UA-GVs), subject to active leaders and switching topologies, is addressed via reinforcement learning. The quadrotors are introduced to achieve predetermined time-varying formation and the ground vehicles are designed to move into the convex hull spanned by the quadrotor formation. The quadrotor dynamics is underactuated, and the UA-GV system involves nonlinear dynamics and uncertain dynamical parameters. Distributed observers are developed for each vehicle to provide the position reference under the effects of switching topologies and unpredictable maneuvers of the leaders. Optimal control laws are proposed without accurate information of the dynamical models of the UA-GVs using reinforcement learning. Simulation results of a heterogeneous UA-GV team are presented and the superiority of the proposed data-driven optimal control laws is validated. (c) 2023 Elsevier Ltd. All rights reserved.

引用

页数：10

共 34 条

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