Unmanned Aerial Vehicles Formation Using Learning Based Model Predictive Control

被引:24
|
作者
Hafez, Ahmed T. [1 ]
Givigi, Sidney N. [2 ]
Yousefi, Shahram [3 ]
机构
[1] Mil Tech Coll, Dept Elect Engn, Cairo, Egypt
[2] Royal Mil Coll Canada, Dept Elect & Comp Engn, Kingston, ON, Canada
[3] Queens Univ, Dept Elect & Comp Engn, Kingston, ON, Canada
关键词
Model predictive control; learning control; unmanned aerial vehicles; vehicle formation; optimization; cooperative robotics; SYSTEMS;
D O I
10.1002/asjc.1774
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 solution for the formation flight problem for multiple unmanned aerial vehicles (UAVs) cooperating to execute a required mission. Learning Based Model Predictive Control (LBMPC) is implemented on the team of UAVs in order to accomplish the required formation. All flight simulations respect Reynold's rules of flocking to avoid UAV collisions with nearby flockmates, match the team members velocity and stay close to each other during the formation. The main contribution of this paper lies in the application of LBMPC to solve the problem of formation for an autonomous team of UAVs. The proposed solution is theoretically, by the application of analysis to the problem, demonstrated to be stable. Moreover, simulations support the findings of the paper. The main contributions of this paper are the proposed LBMPC formulation for formation of vehicles with uncertainty in their models, and the theoretical analysis of the solution.
引用
收藏
页码:1014 / 1026
页数:13
相关论文
共 50 条
  • [31] Event-based formation control for multiple unmanned aerial vehicles under directed topology
    Yin, Tingting
    Gu, Zhou
    Yan, Shen
    ISA TRANSACTIONS, 2023, 137 : 111 - 121
  • [32] Research on Model Predictive Control-based Trajectory Tracking for Unmanned Vehicles
    Yuan, Shoutong
    Zhao, Pengchao
    Zhang, Qingyu
    Hu, Xin
    2019 4TH INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTICS ENGINEERING (ICCRE), 2019, : 79 - 86
  • [33] Robust Hierarchical Formation Control of Unmanned Aerial Vehicles via Neural-Based Observers
    Fei, Yang
    Sun, Yuan
    Shi, Peng
    DRONES, 2022, 6 (02)
  • [34] Cooperative Unmanned Aerial Vehicles Formation via Decentralized LBMPC
    Hafez, Ahmed T.
    Givigi, Sidney N.
    Yousefi, Shahram
    Noureldin, Aboelmaged
    2015 23RD MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED), 2015, : 377 - 383
  • [35] Applying Learning Systems Theory to Model Cognitive Unmanned Aerial Vehicles
    Cody, Tyler
    Beling, Peter A.
    2023 IEEE COGNITIVE COMMUNICATIONS FOR AEROSPACE APPLICATIONS WORKSHOP, CCAAW, 2023,
  • [36] Tight formation control of multiple unmanned aerial vehicles through an adaptive control method
    Wang, Yin
    Wang, Daobo
    SCIENCE CHINA-INFORMATION SCIENCES, 2017, 60 (07)
  • [37] Fault-Tolerant Control for Cooperative Unmanned Aerial Vehicles Formation Via Fuzzy Logic
    Hafer, Ahmed Taimour
    Kamel, Mohamed A.
    2016 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS), 2016, : 1261 - 1266
  • [38] Virtual Electric Dipole Field Applied to Autonomous Formation Flight Control of Unmanned Aerial Vehicles
    Ambroziak, Leszek
    Ciezkowski, Maciej
    SENSORS, 2021, 21 (13)
  • [39] Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle
    Hu, Chaofang
    Zhao, Lingxue
    Cao, Lei
    Tjan, Patrick
    Wang, Na
    MEASUREMENT & CONTROL, 2020, 53 (3-4) : 501 - 518
  • [40] Multiple Unmanned Aerial Vehicles Coalition Formation and Control for Collaborative Defense Mission
    Chen, Lin
    Wei, Chen
    Duan, Haibin
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2024, 60 (05) : 6095 - 6109