Distributed Adaptive Human-in-the-Loop Event-Triggered Formation Control for QUAVs With Quantized Communication

To improve the safety and reliability of quadrotor unmanned aerial vehicles (QUAVs) with limited communication, system uncertainties, and unknown external disturbances in a highly uncertain and safety-critical environment, a distributed adaptive human-in-the-loop event-triggered (ET) formation contr...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial informatics 2023-06, Vol.19 (6), p.7572-7582
Main Authors: Guo, Hongzhen, Chen, Mou, Jiang, Yuhan, Lungu, Mihai
Format: Article
Language:English
Subjects:
Actuators
Adaptive control
Closed loop systems
Closed loops
Communication
Communications systems
Control systems design
Controllers
Distributed formation control
Disturbance observers
event triggered (ET)
Feedback control
Formation control
Gesture recognition
Human in the loop
Hysteresis
Informatics
Neural networks
quadrotor unmanned aerial vehicle (QUAV)
quantization
Quantization (signal)
Radial basis function
Safety
Safety critical
Uncertainty
Unmanned aerial vehicles
Unmanned helicopters
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To improve the safety and reliability of quadrotor unmanned aerial vehicles (QUAVs) with limited communication, system uncertainties, and unknown external disturbances in a highly uncertain and safety-critical environment, a distributed adaptive human-in-the-loop event-triggered (ET) formation controller is proposed. The nonautonomous leader is controlled by receiving control commands decided by the gesture recognition system, and the followers are controlled indirectly via the connected communication network. Thus, the safety and flexibility of the closed-loop system are improved. The designed controller is quantized and then sent to the actuator only at the ET instants to further reduce the network burden. The radial basis function neural network is used to approximate the system uncertainties. The unknown approximation error and the unknown external disturbance are viewed as a compound disturbance compensated by the high-order disturbance observer. In addition, the uniformly ultimately bounded stability of the closed-loop system is achieved through the Lyapunov method. Finally, comparative experiments are implemented on the QUAVs to demonstrate the validity of the presented control scheme.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3211508