Event-Based Finite-Time Neural Control for Human-in-the-Loop UAV Attitude Systems

Guohuai Lin, Hongyi Li, Choon Ki Ahn, Deyin Yao

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


This article focuses on the event-based finite-time neural attitude consensus control problem for the six-rotor unmanned aerial vehicle (UAV) systems with unknown disturbances. It is assumed that the six-rotor UAV systems are controlled by a human operator sending command signals to the leader. A disturbance observer and radial basis function neural networks (RBF NNs) are applied to address the problems regarding external disturbances and uncertain nonlinear dynamics, respectively. In addition, the proposed finite-time command filtered (FTCF) backstepping method effectively manages the issue of ``explosion of complexity,'' where filtering errors are eliminated by the error compensation mechanism. In addition, an event-triggered mechanism is considered to alleviate the communication burden between the controller and the actuator in practice. It is shown that all signals of the six-rotor UAV systems are bounded and the consensus errors converge to a small neighborhood of the origin in finite time. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Publication statusAccepted/In press - 2022


  • Attitude control
  • Autonomous aerial vehicles
  • Backstepping
  • Control systems
  • Convergence
  • Disturbance observer
  • Error compensation
  • Explosions
  • finite-time command filtered (FTCF) backstepping
  • human-in-the-loop (HiTL)
  • radial basis function neural networks (RBF NNs)
  • unmanned aerial vehicle (UAV) systems.

ASJC Scopus subject areas

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


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