Abstract
Fault diagnosis (FD) is one of the main roles of fault-tolerant control (FTC) systems. An FD should not only identify the presence of a fault, but also quantify its magnitude and location. In this work, we present a robust fault diagnosis method for quadcopter unmanned aerial vehicle (UAV) actuator faults. The state equation of the quadcopter UAV is examined as a nonlinear system. An adaptive sliding mode Thau observer (ASMTO) method is proposed to estimate the fault magnitude through an adaptive algorithm. We then obtain the design matrices and parameters using the linear matrix inequalities (LMI) technique. Finally, experimental results are presented to show the advantages of the proposed algorithm. Unlike previous research on quadcopter UAV FD systems, our study is based on ASMTO and can, therefore, determine the time variability of a fault in the presence of external disturbances.
Highlights
Quadcopter unmanned aerial vehicles (UAVs) have been used in a variety of applications, due to their numerous advantages, such as small size, agility, low cost, mechanical simplicity, and indoor and outdoor operability, which have led to their increased popularity compared to other UAV systems
The topic of fault-tolerant control (FTC) has received a large amount of attention in the community, which led to quadcopter UAVs that are less error-prone and, more reliable during flight
Few studies focused on the problem of fault diagnosis in a real quadcopter UAV, observer based on H ∞, and demonstrated the effectiveness of the proposed scheme, this method verified through real flight data
Summary
Quadcopter unmanned aerial vehicles (UAVs) have been used in a variety of applications, due to their numerous advantages, such as small size, agility, low cost, mechanical simplicity, and indoor and outdoor operability, which have led to their increased popularity compared to other UAV systems. Zhang [12,13] proposed a method for fault estimation based on a Kalman filter, but their approach is effective approaches, such as sliding mode observer [14,15], neural network [16,17], and adaptive of insufficient robustness with regard to disturbances if the transfer matrices are inaccurate. Recent studies [20,21] used fuzzy methods for fault diagnosis problems, but these observer [18,19], have been investigated, but none of these approaches focused on a real quadcopter approaches do not focus on real quadcopters, and may be overly complex to implement in a flight. Few studies focused on the problem of fault diagnosis in a real quadcopter UAV, observer based on H ∞ , and demonstrated the effectiveness of the proposed scheme, this method verified through real flight data.
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