Neural Adaptive Distributed Formation Control of Nonlinear Multi-UAVs With Unmodeled Dynamics

Yajing Yu, Jian Guo, Choon Ki Ahn, Zhengrong Xiang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The problem of neural adaptive distributed formation control is investigated for quadrotor multiple unmanned aerial vehicles (UAVs) subject to unmodeled dynamics and disturbance. The quadrotor UAV system is divided into two parts: the position subsystem and the attitude subsystem. A virtual position controller based on backstepping is designed to address the coupling constraints and generate two command signals for the attitude subsystem. By establishing the communication mechanism between the UAVs and the virtual leader, a distributed formation scheme, which uses the UAVs' local information and makes each UAV update its position and velocity according to the information of neighboring UAVs, is proposed to form the required formation flight. By designing a neural adaptive sliding mode controller (SMC) for multi-UAVs, the compound uncertainties (including nonlinearities, unmodeled dynamics, and external disturbances) are compensated for to guarantee good tracking performance. The Lyapunov theory is used to prove that the tracking error of each UAV converges to an adjustable neighborhood of zero. Finally, the simulation results demonstrate the effectiveness of the proposed scheme.

Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Aerodynamics
  • Autonomous aerial vehicles
  • Distributed formation control
  • Heuristic algorithms
  • multi-unmanned aerial vehicles (UAVs)
  • nonlinearities
  • Rotors
  • sliding mode control (SMC)
  • Switches
  • Uncertainty
  • unmodeled dynamics.
  • Vehicle dynamics

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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