Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs

被引:73
作者
Cai, Zhihao [1 ]
Wang, Longhong [1 ]
Zhao, Jiang [1 ]
Wu, Kun [2 ]
Wang, Yingxun [1 ]
机构
[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100083, Peoples R China
[2] Beihang Univ, Flying Coll, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Distributed Model Predictive Control (MPC); Event-triggered mechanism; Formation control; Obstacle avoidance; Unmanned Aerial Vehicles (UAVs); Virtual Target Guidance (VTG); OBSTACLE AVOIDANCE; MOVING TARGETS; SEARCH; TRACKING;
D O I
10.1016/j.cja.2019.07.016
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The paper proposes a Virtual Target Guidance (VTG)-based distributed Model Predictive Control (MPC) scheme for formation control of multiple Unmanned Aerial Vehicles (UAVs). First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem (FHOCP) can be solved by swarm intelligent optimization algorithm. Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance. Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function. Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method. (C) 2019 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:1037 / 1056
页数:20
相关论文
共 32 条
[1]  
Ali ZA, 2018, INT CONF SYST INFORM, P61, DOI 10.1109/ICSAI.2018.8599350
[2]  
Anna P, 2018, OPTIMAL CONTROL APPL, V39, P343
[3]   Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control [J].
Cai, Zhihao ;
Lou, Jiang ;
Zhao, Jiang ;
Wu, Kun ;
Liu, Ningjun ;
Wang, Ying Xun .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 128 :636-654
[4]   Control and dynamics analysis for miniature autogyro and compound autogyro [J].
Cai, Zhihao ;
Liu, Ningjun ;
Zhao, Jiang ;
Wang, Yingxun .
SCIENCE CHINA-INFORMATION SCIENCES, 2019, 62 (01)
[5]   Formation Control of Multiple Unmanned Aerial Vehicles by Event-Triggered Distributed Model Predictive Control [J].
Cai, Zhihao ;
Zhou, Hui ;
Zhao, Jiang ;
Wu, Kun ;
Wang, Yingxun .
IEEE ACCESS, 2018, 6 :55614-55627
[6]   UAV Formation Flight Based on Nonlinear Model Predictive Control [J].
Chao, Zhou ;
Zhou, Shao-Lei ;
Ming, Lei ;
Zhang, Wen-Guang .
MATHEMATICAL PROBLEMS IN ENGINEERING, 2012, 2012
[7]   A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems [J].
Ding, Xilun ;
Guo, Pin ;
Xu, Kun ;
Yu, Yushu .
CHINESE JOURNAL OF AERONAUTICS, 2019, 32 (01) :200-214
[8]   A novel sliding mode controller for small-scale unmanned helicopters with mismatched disturbance [J].
Fang, Xing ;
Wu, Aiguo ;
Shang, Yujia ;
Dong, Na .
NONLINEAR DYNAMICS, 2016, 83 (1-2) :1053-1068
[9]   Multiple Moving Targets Surveillance Based on a Cooperative Network for Multi-UAV [J].
Gu, Jingjing ;
Su, Tao ;
Wang, Qiuhong ;
Du, Xiaojiang ;
Guizani, Mohsen .
IEEE COMMUNICATIONS MAGAZINE, 2018, 56 (04) :82-89
[10]   Multi-UAV Oxyrrhis Marina-Inspired Search and Dynamic Formation Control for Forest Firefighting [J].
Harikumar, K. ;
Senthilnath, J. ;
Sundaram, Suresh .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2019, 16 (02) :863-873