Leader-follower formation of light-weight UAVs with novel active disturbance rejection control

Jiacheng Li, Junmin Liu, Shuaiqi Huangfu, Guoyan Cao, Dengxiu Yu

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

Considering that the formation composed of light-weight UAVs is highly susceptible to the interference, which may from the changes in the external environment and the uncertainty of system, a formation control method based on the inner and outer loops is proposed. In the inner loop, the single UAV stable flight can be achieved via controlling the state variables of UAV angles. In the outer loop, we can achieve multi-UAVs cooperative flight through the leader-follower strategy. In this paper, we mainly focus on the stability control of each member UAV, which is the basic composition of formation control. For this purpose, an optimized active disturbance rejection control (ADRC) is proposed which combined an improved prescribed-time extended state observer (PTESO) and weight optimization module based on broad learning. In this way, the single UAV can dynamically adjust the control weights according to different wind degrees, so that the influence of environmental changes on the control system is reduced. Then, the effectiveness and superiority of the proposed formation control methods are verified by simulations. The stability enhancement control method proposed in this paper provides a new and effective theoretical support for the actual control of the light-weight UAV formation, which has a well engineering application prospect.

Original languageEnglish
Pages (from-to)577-591
Number of pages15
JournalApplied Mathematical Modelling
Volume117
DOIs
StatePublished - May 2023

Keywords

  • Broad learning
  • Environment disturbance
  • Leader-follower
  • Light-weight UAV
  • Optimized ADRC
  • Prescribed-time

Fingerprint

Dive into the research topics of 'Leader-follower formation of light-weight UAVs with novel active disturbance rejection control'. Together they form a unique fingerprint.

Cite this