Distributed data-driven UAV formation control via evolutionary games: Experimental results

被引:18
作者
Barreiro-Gomez, J. [1 ,2 ]
Mas, I [3 ,4 ]
Giribet, J., I [5 ,6 ]
Moreno, P. [5 ,6 ]
Ocampo-Martinez, C. [7 ]
Sanchez-Pena, R. [3 ,4 ]
Quijano, N. [8 ]
机构
[1] New York Univ Abu Dhabi, NYUAD Res Inst, POB 129188, Abu Dhabi, U Arab Emirates
[2] New York Univ Abu Dhabi, Engn Div, Learning & Game Theory Lab L&G Lab, Saadiyat Campus POB 129188, Abu Dhabi, U Arab Emirates
[3] Consejo Nacl Invest Cient & Tecn, Av Madero 399, Buenos Aires, DF, Argentina
[4] Inst Tecnol Buenos Aires, Av Madero 399, Buenos Aires, DF, Argentina
[5] Consejo Nacl Invest Cient & Tecn, Inst Argentino Matemat, Paseo Colon 850, Buenos Aires, DF, Argentina
[6] Univ Buenos Aires, Paseo Colon 850, Buenos Aires, DF, Argentina
[7] Univ Politecn Cataluna, Inst Robot & Informat Ind CSIC UPC, Dept Automat Control, Llorens I Artigas 4-6, Barcelona 08028, Spain
[8] Univ Los Andes, Sch Elect & Elect Engn, Carrera 1A 18A-10, Bogota, Colombia
关键词
UNMANNED AERIAL VEHICLES; SLIDING MODE CONTROL; ATTITUDE-CONTROL; MULTIPLE UAVS; OPTIMIZATION; DESIGN;
D O I
10.1016/j.jfranklin.2021.05.002
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This work proposes a novel data-driven distributed formation-control approach based on multi-population evolutionary games, which is structured in a leader-follower scheme. The methodology considers a time-varying communication graph that describes how the multiple agents share information to each other. We present stability guarantees for configurations given by time-varying interaction networks, making the proposed method suitable for real-world problems where communication constraints change along the time. Additionally, the proposed formation controller allows for an agent to leave or enter the group without the need to modify the behaviors of other agents in the group. This game-theoretical approach is evaluated through numerical simulations and real outdoors experimental results using a fleet of aerial autonomous vehicles, showing the control performance. (C) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:5334 / 5352
页数:19
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