Formation control for multi-UAVs systems based on Kullback-Leibler divergence

被引:5
|
作者
Liao, Wei [1 ]
Wei, Xiaohui [1 ,2 ]
Lai, Jizhou [3 ]
Sun, Hao [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Key Lab Fundamental Sci Natl Def Adv Design Techn, Nanjing, Jiangsu, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, State Key Lab Mech & Control Mech Struct, Nanjing, Jiangsu, Peoples R China
[3] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing, Jiangsu, Peoples R China
基金
中国博士后科学基金;
关键词
Formation control; multi-UAVs system; Kullback-Leibler divergence; RIGID FORMATIONS;
D O I
10.1177/0142331219878581
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 paper presents a formation control method for multi unmanned aerial vehicles (UAVs) systems. The first step is to design two probability density functions describing to the desired formation and current formation, respectively. Then, through minimizing the Kullback-Leibler divergence, this method is able to bring the UAVs to a desired formation and stabilizes the desired formation in all initial conditions except the case where a pair of UAVs are in the same initial position. The gradient of Kullback-Leibler divergence is calculated using Monte Carlo method, by means of which it is not necessary to preplan route for every UAV and to take extra measure to avoid collisions between any two UAVs during the motion. At the end of this paper, the proposed method is adopted to carry out to some numerical simulations in a two-dimensional space and a three-dimensional space, respectively, to illustrate the effectiveness of the method. Conclusions show that the formation of the UAVs can converge to the desired formation under the control law proposed in this paper.
引用
收藏
页码:598 / 603
页数:6
相关论文
共 50 条
  • [1] Renyi Divergence and Kullback-Leibler Divergence
    van Erven, Tim
    Harremoes, Peter
    IEEE TRANSACTIONS ON INFORMATION THEORY, 2014, 60 (07) : 3797 - 3820
  • [2] The fractional Kullback-Leibler divergence
    Alexopoulos, A.
    JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2021, 54 (07)
  • [3] BOUNDS FOR KULLBACK-LEIBLER DIVERGENCE
    Popescu, Pantelimon G.
    Dragomir, Sever S.
    Slusanschi, Emil I.
    Stanasila, Octavian N.
    ELECTRONIC JOURNAL OF DIFFERENTIAL EQUATIONS, 2016,
  • [4] On the Interventional Kullback-Leibler Divergence
    Wildberger, Jonas
    Guo, Siyuan
    Bhattacharyya, Arnab
    Schoelkopf, Bernhard
    CONFERENCE ON CAUSAL LEARNING AND REASONING, VOL 213, 2023, 213 : 328 - 349
  • [5] Kullback-Leibler Divergence Revisited
    Raiber, Fiana
    Kurland, Oren
    ICTIR'17: PROCEEDINGS OF THE 2017 ACM SIGIR INTERNATIONAL CONFERENCE THEORY OF INFORMATION RETRIEVAL, 2017, : 117 - 124
  • [6] GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback-Leibler Divergence
    Basan, Elena
    Basan, Alexandr
    Nekrasov, Alexey
    Fidge, Colin
    Sushkin, Nikita
    Peskova, Olga
    DRONES, 2022, 6 (01)
  • [7] Modulation Classification Based on Kullback-Leibler Divergence
    Im, Chaewon
    Ahn, Seongjin
    Yoon, Dongweon
    15TH INTERNATIONAL CONFERENCE ON ADVANCED TRENDS IN RADIOELECTRONICS, TELECOMMUNICATIONS AND COMPUTER ENGINEERING (TCSET - 2020), 2020, : 373 - 376
  • [8] Model Fusion with Kullback-Leibler Divergence
    Claici, Sebastian
    Yurochkin, Mikhail
    Ghosh, Soumya
    Solomon, Justin
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 119, 2020, 119
  • [9] Kullback-Leibler Divergence Metric Learning
    Ji, Shuyi
    Zhang, Zizhao
    Ying, Shihui
    Wang, Liejun
    Zhao, Xibin
    Gao, Yue
    IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (04) : 2047 - 2058
  • [10] Nonparametric Estimation of Kullback-Leibler Divergence
    Zhang, Zhiyi
    Grabchak, Michael
    NEURAL COMPUTATION, 2014, 26 (11) : 2570 - 2593