—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.
|Number of pages
|IEEE Transactions on Neural Networks and Learning Systems
|Published - 2023 Dec 1
Bibliographical notePublisher Copyright:
© 2022 IEEE.
- Disturbance observer
- 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
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications