Robust fault detection and adaptive fixed-time fault-tolerant control for quadrotor UAVs

• Published in: Robotics and autonomous systems Vol. 179; p. 104747
• Main Authors: Mazare, Mahmood, Taghizadeh, Mostafa, Ghaf-Ghanbari, Pegah, Davoodi, Ehsan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024
• Subjects: Adaptive control; Fault detection; Fault-tolerant control; Kalman filter; Quadrotor; Fault detection; Fault-tolerant control; Adaptive control; Kalman filter; Quadrotor
• ISSN: 0921-8890; 1872-793X
• DOI: 10.1016/j.robot.2024.104747

•Guarantees fixed-time stability; eliminates chattering issues of SMC.•Adaptive algorithm for simultaneous fault accommodation and disturbance suppression.•Estimates upper bound of lumped uncertainties; no need for known uncertainty bounds.•Uses EKF observer for dynamic fault detection with adaptive threshold technique. This note scrutinizes an adaptive fault-tolerant control (FTC) approach tailored for unmanned aerial vehicles (UAVs), addressing the critical need for both fault accommodation and disturbance suppression. Departing from traditional reliance on robust discontinuous control strategies prone to chattering and demanding precise uncertainty bounds, our FTC method ensures fixed-time stability, guaranteeing the convergence of attitude tracking errors to zero. Central to our approach is an adaptive algorithm adept at concurrently estimating unknown actuator faults and upper bounds of lumped uncertainties. Moreover, our adaptive schemes accurately estimate the upper bound of the lumped uncertainty term, encompassing model uncertainties, external disturbances, and unmodeled dynamics, thereby eliminating the need for assuming known bounds on uncertainties. Stability analysis under the developed control law is thoroughly performed using the Lyapunov stability theory. Notably, our strategy employs an extended Kalman filter (EKF) observer for state estimation and fault detection, facilitating fault detection through an adaptive threshold technique dynamically adjusted based on real-time mean and variance of the residual signal. Through comprehensive simulation and experimental validations, our proposed methodology demonstrates significant advancements in ensuring safety and reliability in UAVs.