Formation control of multi-UAV based on distributed model predictive control algorithm

被引：0
|
作者：
Zhao C.-L. [1 ]
Dai S.-W. [1 ]
Zhao G.-R. [1 ]
Gao C. [1 ]
Liu S. [2 ]
机构：
[1] Coastal Defense College, Naval Aviation University, Yantai
[2] PLA 92635 Unit, Qingdao
来源：
Kongzhi yu Juece/Control and Decision | 2022年 / 37卷 / 07期
关键词：
Distributed control; Formation control; Leader-follower method; Model predictive control; Quadrotor; UAV;
D O I：
10.13195/j.kzyjc.2021.0447
中图分类号：
学科分类号：
摘要：
This paper presents a distributed model predictive control algorithm for the formation and maintenance of multi-quadrotor during the cruise fight. That is dealing with the formation control using the rolling optimization method. Firstly, a linear time-invariant formation motion model is established. Then using the leader-follower strategy, a distributed model predictive controller is designed by introducing the assumed state trajectory of itself and neighbors to the cost function, which is in the case of considering the state and input constraints, without considering the communication delay, external interference and noise. Unmanned aerial vehicles (UAVs) interact with local information based on a directional, time-invariant communication topology. Based on the controller, UAVs can quickly form a pre-set formation and maintain it while tracking the target trajectory. To ensure the stability of the system, the terminal equality constraint is introduced. Then taking the cost function as the Lyapunov function, the sufficient conditions for the asymptotic stability of the formation system are given. Finally, simulations with six UAVs demonstrate the effectiveness and superiority of the proposed algorithm. Copyright ©2022 Control and Decision.
引用
收藏
页码：1763 / 1771
页数：8
相关论文
共 21 条
  • [1] Shen L C, Niu Y F, Zhu H Y., Theories and methods of autonomous cooperative control for multiple UAVs, pp. 1-8, (2018)
  • [2] Liang X L, Zhang J Q, Lv N., UAV swarms, pp. 40-49, (2018)
  • [3] Kotov K Y, Mal'Tsev A S, Nesterov A A, Et al., Decentralized control of quadrotors in a leader-follower formation, Optoelectronics, Instrumentation and Data Processing, 53, 1, pp. 21-25, (2017)
  • [4] Kownacki C., Multi-UAV flight using virtual structure combined with behavioral approach, Acta Mechanica et Automatica, 10, 2, pp. 92-99, (2016)
  • [5] Qiu H X, Duan H B, Fan Y M., Multiple unmanned aerial vehicle autonomous formation based on the behavior mechanism in pigeon flocks, Control Theory & Applications, 32, 10, pp. 1298-1304, (2015)
  • [6] Wang X K, Liu Z H, Cong Y R, Et al., Miniature fixed-wing UAV swarms: Review and outlook, Acta Aeronautica et Astronautica Sinica, 41, 4, pp. 20-45, (2020)
  • [7] Jia Y N, Tian S Y, Li Q., Recent development of unmanned aerial vehicle swarms, Acta Aeronautica et Astronautica Sinica, 41, pp. 4-14, (2020)
  • [8] Lai Y H, Li R, Shi Y J, Et al., On the study of a multi-quadrotor formation control with triangular structure based on Graph theory, Control Theory & Applications, 35, 10, pp. 1530-1537, (2018)
  • [9] Dong X W, Zhou Y, Ren Z, Et al., Time-varying formation control for unmanned aerial vehicles with switching interaction topologies, Control Engineering Practice, 46, pp. 26-36, (2016)
  • [10] Cai Z H, Wang L H, Zhao J, Et al., Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs, Chinese Journal of Aeronautics, 33, 3, pp. 1037-1056, (2020)
123