Fault-tolerant Flight Control and Guidance Systems

Ducard, Guillaume

doi:10.1007/978-1-84882-561-1

Cited by 272 publications

(80 citation statements)

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“…In the literature there exists numerous approaches to fault detection and isolation (FDI) techniques applied for the detection and isolation of actuator and sensor faults on UAVs, see Bateman et al (2011), Ducard (2009, and Ducard and Geering (2008) for the former and for the later see Fravolini et al (2009). Detection of control surface defects was studied in Blanke and Hansen (2013), where fault parameter adaptation and fault diagnosis was attempted simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Wing Icing Through Lift and Drag Coefficient Change Detection for Small

Sørensen

Blanke²,

Johansen

2015

IFAC-PapersOnLine

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This paper address the issue of structural change, caused by ice accretion, on UAVs by utilising a Neyman Pearson (NP) based statistical change detection approach, for the identification of structural changes of fixed wing UAV airfoils. A structural analysis is performed on the nonlinear aircraft system and residuals are generated, where a generalised likelihood ratio test is applied to detect faults. Numerical simulations demonstrate a robust detection with adequate balance between false alarm rate and sensitivity.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Wing Icing Through Lift and Drag Coefficient Change Detection for Small

Sørensen

Blanke²,

Johansen

2015

IFAC-PapersOnLine

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“…General treatments of the subject were covered by (Edwards et al, 2010) and (Zolghadri et al, 2013) highlighting some of the major advances within in recent years. A full treatment of how to accommodate to faults affecting the control of a UAS was shown in (Ducard and Geering, 2008) and (Ducard, 2009) where manoeuvrability was the main focus.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Diagnosis of Critical Faults in Unmanned Aerial Vehicles

Hansen

Blanke

Adrian³

2014

IFAC Proceedings Volumes

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Unmanned Aerial Vehicles (UAVs) need a large degree of tolerance towards faults.If not diagnosed and handled in time, many types of faults can have catastrophic consequences if they occur during flight. Prognosis of faults is also valuable and so is the ability to distinguish the severity of the different faults in terms of both consequences and the frequency with which they appear. In this paper flight data from a fleet of UAVs is analysed with respect to certain faults and their frequency of appearance. Data is taken from a group of UAV's of the same type but with small differences in weight and handling due to different types of payloads and engines used. Categories of critical faults, that could and have caused UAV crashes are analysed and requirements to diagnosis are formulated. Faults in air system sensors and in control surfaces are given special attention. In a stochastic framework, and based on a large number of data logged during flights, diagnostic methods are employed to diagnose faults and the performance of these fault detectors are evaluated against flight data. The paper demonstrates a significant potential for reducing the risk of unplanned loss of remotely piloted vehicles used by the Danish Navy for target practice.

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“…These include [9] and [10] where the focus was on how faults directly affect the UAV's ability to maneuver. Reconfigurations of the control and guidance systems were done to accommodate the reduced capabilities of the aircraft.…”

Section: Introductionmentioning

confidence: 99%

Control surface fault diagnosis with specified detection probability — Real event experiences

Hansen

Blanke

2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-Diagnosis of actuator faults is crucial for aircraft since loss of actuation can have catastrophic consequences. For autonomous aircraft the steps necessary to achieve fault tolerance is limited when only basic and non-redundant sensor and actuators suites are present. Through diagnosis that exploits analytical redundancies it is, nevertheless, possible to cheaply enhance the level of safety. This paper presents a method for diagnosing control surface faults by using basic sensors and hardware available on an autonomous aircraft. The capability of fault diagnosis is demonstrated obtaining desired levels of false alarms and detection probabilities. Self-tuning residual generators are employed for diagnosis and are combined with statistical change detection to form a setup for robust fault diagnosis. On-line estimation of test statistics is used to obtain a detection threshold and a desired false alarm probability. A data based method is used to determine the validity of the methods proposed. Verification is achieved using real data and shows that the presented diagnosis method is efficient and could have avoided incidents where faults led to loss of aircraft.

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Fault-tolerant Flight Control and Guidance Systems

Cited by 272 publications

References 0 publications

Diagnosis of Wing Icing Through Lift and Drag Coefficient Change Detection for Small

Diagnosis of Wing Icing Through Lift and Drag Coefficient Change Detection for Small

A Framework for Diagnosis of Critical Faults in Unmanned Aerial Vehicles

Control surface fault diagnosis with specified detection probability — Real event experiences

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