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Learning-observer-based resilient fault-tolerant control for quadrotor unmanned aerial vehicles
Received date: 2024-10-08
Revised date: 2025-01-03
Accepted date: 2025-02-19
Online published: 2025-02-28
Supported by
National Natural Science Foundation of China(52472463);China University Industry-University-Research Innovation Fund(2024HT004);The Open Fund of State Key Laboratory of Robotics and Intelligent Systems(2024-O01);The Discipline Integration and Innovation Project of China Agricultural University;The Independent Innovation Research Fund for Graduate Students of China Agricultural University
This paper proposed a learning-observer-based resilient fault-tolerant control scheme for the control problem of quadrotor unmanned aerial vehicles with sensors and actuators faults. By processing the fault output signals of sensors， and based on coordinate changes， an extended system is constructed. Then， to estimate the system fault function and actual output online， a learning observer is designed using sensor fault outputs. Further， an adaptive neural network backstepping resilient fault-tolerant controller is designed to achieve the actual system output tracking the reference signal in the event of sensor faults and additive actuator faults in the unmanned aerial vehicle. Based on the Lyapunov stability theory， the stability of closed-loop system is proven. Finally， simulations and outdoor experiments are conducted using quadrotor unmanned aerial vehicle and the results validated the effectiveness of the proposed method.
Tao CHEN , Jian CHEN . Learning-observer-based resilient fault-tolerant control for quadrotor unmanned aerial vehicles[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(11) : 531346 -531346 . DOI: 10.7527/S1000-6893.2024.31346
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