The SMAC Fault Detection and Isolation Scheme: Discussions, Improvements, and Application to a UAV

International audienceThis paper presents extensions made to the SingleModel Active Fault Detection and Isolation System (SMACFDI)presented previously by the author. The SMAC-FDI systemis designed to efficiently detect and isolate a faulty actuatorin a system, such as a small aircraft. The implementation ofthe filter, which generates state residuals has been revisited andimproved. Moreover, the paper presents a complete parameter tuningstrategy for the FDI system. In addition, the robustnessof the SMAC-FDI method is tested in the presence of modeluncertainties and realistic sensor noise. Finally, the presentedconcepts are backed-up by simulations of a small unmannedaircraft experiencing successive actuator failures

Actuator Fault Detection in UAVs, in Handbook of Unmanned Aircraft

International audienceFuture unmanned aerial vehicles (UAVs) will be designed toachieve their missions with increased efficiency, safety, andsecurity. To this end, an efficient fault detection and isolation(FDI) system should be capable of monitoring the health statusof the aircraft. Fault-tolerant control systems for small andlow-cost UAVs should not increase significantly the numberof actuators or sensors needed to achieve the safer operation.This chapter is dedicated to actuator fault detection systemsfor UAVs, with two main requirements: real-time capabilityand modularity. After defining the terminology employed inthis field, this chapter reviews some commonly used techniquesin FDI systems. The chapter continues by presenting brieflythe mathematical model of a UAV which will serve as a basisfor the design of two actuator FDI systems. The first methodpresents and illustrates the multiple-model approach, whereasthe second method presents an FDI system which is basedon a single model. Both methods have been enhanced by amechanism that actively tests actuators in order to efficientlydetect and isolate actuator faults and failures. This chapterexplains the advantages and drawbacks of each method anddiscusses issues of robustness against model uncertainties andexternal perturbation. In addition, aspects of computationalload are addressed. Finally, the FDI systems of this chapter areapplied to a realistic model of an unmanned aircraft, and theperformance of the methods is shown in simulation

SMAC-FDI: Single Model Active Fault Detection and Isolation System for Unmanned Aircraft

International audienceThis article presents a single model active fault detection and isolation system (SMAC-FDI) which is designed to efficientlydetect and isolate a faulty actuator in a system, such as a small (unmanned) aircraft. This FDI system is based on a singleand simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. Theseresiduals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for thesignature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previousresiduals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy forthis FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successiveactuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensornoise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and itsability to run on small microcontrollers

Smac–Fdi: A Single Model Active Fault Detection and Isolation System for Unmanned Aircraft

This article presents a single model active fault detection and isolation system (SMAC-FDI) which is designed to efficiently detect and isolate a faulty actuator in a system, such as a small (unmanned) aircraft. This FDI system is based on a single and simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. These residuals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for the signature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previous residuals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy for this FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successive actuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensor noise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and its ability to run on small microcontrollers

Low-cost 3D Laser Design and Evaluation with Mapping Techniques Review

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Limitations in Total Weight for Solar Aircraft Designed for Infinite Flight

International audienceThis paper presents a way to determine a weightfeasibility criterion for solar-powered airplanes designed toperform infinite flight. The criterion is fast and simple tocompute and can be used for initial assessments on aircraftdesign and mission planning. The derivation starts with takingsolar cell weight and efficiency into account, then is expandedto energy-storage and propulsion group considerations. Thisstep-by-step approach can then be used to determine a feasibleweight and predict system performance properties.The presented criterion was validated using data fromthree different unmanned solar airplanes. SkySailor, a 3.2mwingspan, 2.55kg light small type of unmanned airplane,AtlantikSolar weighting 6.7kg and with a wingspan of 5.65mand the Helios HP01 Prototype, with 75.3m wingspan and929kg weight one of the largest unmanned solar aircrafts. Allthree validations proved the concept and granted additionalperformance estimations

Metric Visual-Inertial Ego-Motion Estimation Using Single Optical Flow Feature

International audienceThis paper introduces a state estimation frameworkthat allows estimating the attitude, full metric speed andthe orthogonal metric distance of an IMU-camera system withrespect to a plane. The filter relies only on a single optical flowfeature as well as gyroscope and accelerometer measurements.The underlying assumption is that the observed visual featurelies on a static plane. The orientation of the observed planeis not required to be known a priori and is also estimated atruntime. The estimation framework fuses visual and inertialmeasurements in an Unscented Kalman Filter (UKF). Thetheoretical limitations of the UKF are investigated using anonlinear observability analysis based on Lie-derivatives.Experiments in simulation using realistic sensor noise valuessuccessfully demonstrate the performance of the filter as wellas validate the findings of the observability analysis. It is shownthat the state estimate is converging correctly, even in presenceof substantial initial state errors. To the authors’ knowledge,this paper documents for the first time the estimation of theheading and metric distance to a wall with no range- or bearingsensors, relying solely on optical flow as the only exteroceptivesensing modality. This minimal sensor set, that is both lightweightand low-cost, renders the framework an appealing choicefor the use as a navigation system on a wide range of roboticplatforms, such as ground- or flying robots

Vision-based Gyroscope Fault Detection for UAVs

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