Icing Detection for Small Fixed Wing UAVs using Inflight Aerodynamic Coefficient Estimation

Wenz, Andreas; Johansen, Tor Arne

doi:10.1109/aero.2019.8742134

Cited by 6 publications

(10 citation statements)

References 17 publications

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“…In order to avoid falsely rejecting samples, a maximum number of consecutive outliers can be specified. Using the estimated x x x * k and p p p * k , as well as the inputs u u u k we can now calculate the desired quantities, angle of attack, sideslip angle and airspeed susing equations (8), (17) and (18).…”

Section: Methodsmentioning

confidence: 99%

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Real-Time Moving Horizon Estimation of Air Data Parameters and Wind Velocities for fixed-wing UAVs

Wenz

Johansen

2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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We present a real-time implementation of an estimation algorithm for angle of attack, airspeed and wind velocities estimation on a single board computer. The estimator uses only sensor data from a standard fixed-wing UAV autopilot, which consists of a Global Navigation Satellite System receiver, an inertial measurement unit and a pitot-static tube. This sensor data is fused with a combination of kinematic, aerodynamic and stochstic wind models in a nonlinear moving horizon estimator using numerical optimization. An algorithmic differentiation toolbox and automatic code generation is used to create a realtime capable estimator which is able to run within a UAV on an on-board computer. Hardware in the Loop simulation results show that the latency of the estimator is significantly below the expected wind gust period and gives low root-mean-square estimation errors for angle of attack (0.29 •) , airspeed (0.21m/s) and wind velocities (0.44m/s).

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Section: Methodsmentioning

confidence: 99%

“…Accuracy of the sideslip angle estimate is limited by the lack of a lateral model. Furthermore, the estimator provides estimates of the lift coefficients which can be used for icing detection as shown in [18].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Moving Horizon Estimation of Air Data Parameters and Wind Velocities for fixed-wing UAVs

Wenz

Johansen

2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…T( ) can be obtained from T( ) and T( ) by differentiating each element with respect to time. * It can be seen that the estimated bounds calculated as (15) and (18) are an appropriate combination of the proportional action given by (13) and (16) and the integral action given by (14) and (17).…”

Section: Lpv Proportional Integral Interval Observermentioning

confidence: 99%

“…9,10 However, due to the large power consumption, fault/icing detection schemes 11 with fast and accurate responses are needed for assuring high efficiency. The approaches recently applied to icing detection in aircrafts and UAVs include multiple models, 12,13 statistical methods, 14 aerodynamic coefficient estimators, 15 and environmental monitoring. 16 Unknown input observers (UIOs) are a special class of observers, which allow estimating the state of a system independently of some unknown inputs.…”

Section: Introductionmentioning

confidence: 99%

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Robust fault and icing diagnosis in unmanned aerial vehicles using LPV interval observers

Rotondo

Cristofaro

Johansen

et al. 2018

Intl J Robust & Nonlinear

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Summary This paper proposes a linear parameter varying (LPV) interval unknown input observer for the robust fault diagnosis of actuator faults and ice accretion in unmanned aerial vehicles (UAVs) described by an uncertain model. The proposed interval observer evaluates the set of values for the state, which are compatible with the nominal fault‐free and icing‐free operation and can be designed in such a way that some information about the nature of the unknown inputs affecting the system can be obtained, thus allowing the diagnosis to be performed. The proposed strategy has several advantages. First, the LPV paradigm allows taking into account operating point variations. Second, the noise rejection properties are enhanced by the presence of the integral term. Third, the interval estimation property guarantees the absence of false alarms. Linear matrix inequality–based conditions for the analysis/design of these observers are provided in order to guarantee the interval estimation of the state and the boundedness of the estimation. The developed theory is supported by simulation results, obtained with the uncertain model of a Zagi Flying Wing UAV, which illustrate the strong appeal of the methodology for identifying correctly unexpected changes in the system dynamics due to actuator faults or icing.

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Detection and Isolation of Propeller Icing and Electric Propulsion System Faults in Fixed-Wing UAVs

Haaland

Wenz

Gryte

et al. 2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

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This thesis presents a fault detection and isolation (FDI) framework for detecting propeller icing, and other propulsion faults of unmanned aerial vehicles (UAVs). Such faults are among the main causes for incidents and loss of equipment.A theoretical framework for the proposed FDI is covered extensively. A tuning methodology and an implementation guide are also covered in detail. The method has been tested extensively using a software-in-the-loop simulator. The simulation results have proven to be very successful and this motivates future testing on real data sets. Relation to previous work This is a disclaimer about what is new in this thesis and what has been borrowed or reused. Project workThere will be some content-overlap between this thesis and the project work from the fall of 2020. The project work will be quoted in the relevant parts.Many of the core FDI ideas were developed during the project work. The main contribution of the project work was an in-depth analysis of those ideas. The analysis was aimed at limitations and possibilities. The project left off with a non-functioning method, with many open ends. This thesis presents a refined solution.Software documentation is also covered in this thesis. The software project was started during the project work. This was not documented in the project work. By then most of the code has been refactored many times over. Further, the main data structures were developed after the project work. The project work is therefore not quoted in the software section (7). Previous work by othersThis project builds on previous projects at NTNU. Most prominent is the use of the Ardupilot-Simulink simulator. Much of the simulator was developed by A. W. Wenz, K. Gryte, A. Winter and T. A. Johansen. The simulator is described in Section 5. My contribution to the simulator was the fault dynamics and the aircraft speed control. Some of the core ideas were originally from A. W. Wenz. Wenz never published these ideas, but the ideas are present in the project work. Thus, Wenz is credited through the project work citations.

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Icing Detection for Small Fixed Wing UAVs using Inflight Aerodynamic Coefficient Estimation

Cited by 6 publications

References 17 publications

Real-Time Moving Horizon Estimation of Air Data Parameters and Wind Velocities for fixed-wing UAVs

Real-Time Moving Horizon Estimation of Air Data Parameters and Wind Velocities for fixed-wing UAVs

Robust fault and icing diagnosis in unmanned aerial vehicles using LPV interval observers

Detection and Isolation of Propeller Icing and Electric Propulsion System Faults in Fixed-Wing UAVs

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