Aerodynamic angle estimation: comparison between numerical results and operative environment data

Lerro, Angelo; Battipede, Manuela; Gili, Piero; Brandl, Alberto

doi:10.1007/s13272-019-00417-x

Cited by 17 publications

(12 citation statements)

References 24 publications

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“…In this section results related to flight manoeuvres FT#1–4 are presented because they give a real feedback on the VS performances. The training results are only used for training purposes, e.g., to select the best training among several ones according to consolidated metrics, as showed in previous works [20] and not reported in this paper. Results will be presented as time histories of the errors whereas the current values are not shown for intellectual property reasons.…”

Section: Resultsmentioning

confidence: 99%

“…The virtual sensors are compared in terms of measurement uncertainty required to COTS or MFP probes from Table 2. However, the Smart-ADAHRS technology has been already compared with vanes in [20] without providing any particular evidence of degradation of the ADS performance. However, this analysis will be conducted in future dedicated experiments.…”

Section: Midas Technological Solutionmentioning

confidence: 99%

“…Side findings emerge from the present work and they will affect the future steps of the MIDAS projects. The paper aims to highlight the original elements emerged during the present work and, therefore, neural network architecture optimization will not be discussed here because similar to previous works [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Preliminary Design of a Model-Free Synthetic Sensor for Aerodynamic Angle Estimation for Commercial Aviation

Lerro

Brandl

Battipede

et al. 2019

Sensors

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Heterogeneity of the small aircraft category (e.g., small air transport (SAT), urban air mobility (UAM), unmanned aircraft system (UAS)), modern avionic solution (e.g., fly-by-wire (FBW)) and reduced aircraft (A/C) size require more compact, integrated, digital and modular air data system (ADS) able to measure data from the external environment. The MIDAS project, funded in the frame of the Clean Sky 2 program, aims to satisfy those recent requirements with an ADS certified for commercial applications. The main pillar lays on a smart fusion between COTS solutions and analytical sensors (patented technology) for the identification of the aerodynamic angles. The identification involves both flight dynamic relationships and data-driven state observer(s) based on neural techniques, which are deterministic once the training is completed. As this project will bring analytical sensors on board of civil aircraft as part of a redundant system for the very first time, design activities documented in this work have a particular focus on airworthiness certification aspects. At this maturity level, simulated data are used, real flight test data will be used in the next stages. Data collection is described both for the training and test aspects. Training maneuvers are defined aiming to excite all dynamic modes, whereas test maneuvers are collected aiming to validate results independently from the training set and all autopilot configurations. Results demonstrate that an alternate solution is possible enabling significant savings in terms of computational effort and lines of codes but they show, at the same time, that a better training strategy may be beneficial to cope with the new neural network architecture.

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Section: Resultsmentioning

confidence: 99%

Section: Midas Technological Solutionmentioning

confidence: 99%

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Preliminary Design of a Model-Free Synthetic Sensor for Aerodynamic Angle Estimation for Commercial Aviation

Lerro

Brandl

Battipede

et al. 2019

Sensors

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“…Model-free virtual sensors for the angle of attack measurement are available in the literature, too. They mainly use neural networks [15], [16], which has the drawback to need huge amount of data for the training of the network. This paper is an extension of the work presented in [1], which describes an innovative approach to estimate the angle of attack, developed and patented [17] by the Italian Aerospace Research Centre (CIRA) and ASPEN Avionics.…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

An Innovative Angle of Attack Virtual Sensor for Physical-Analytical Redundant Measurement System Applicable to Commercial Aircraft

Vitale¹,

Corraro²,

Genito³

et al. 2021

Adv. sci. technol. eng. syst. j.

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The angle of attack is a critical flight parameter for commercial aviation aircraft, because automatic envelope protection systems rely on it to keep the aircraft within its safe flight envelope. Faulty measurements of the angle of attack could have catastrophic effects, leading to aircraft loss of control in flight and fatalities, as demonstrated by the recent accidents involving the Boeing 737-MAX. This paper presents a novel approach to the measurement of the angle of attack, which uses one virtual sensor and two physical sensors to implement a physical-analytical redundant system that is robust to a single fault of the physical sensors. The virtual sensor is based on an innovative and reliable estimator of the angle of attack. It was originally developed to provide General Aviation pilot with an accurate indication of trend toward stall, and has been suitably customized to fit its application to commercial aviation. One of the peculiarities of the redundant measurement system is that its implementation on-board several existing commercial aviation aircraft only needs the integration of a software code and does not require any installation of additional physical sensors. The proposed approach demonstrated very interesting performance, assessed in simulation through several Monte Carlo analyses. Its exploitation could contribute to reduce the angle of attack related accidents, improving the safety of the air transport system.

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“…On the contrary, in this field a synthetic sensor (also called virtual sensor) is an estimator able to provide AOA and/or AOS with an sufficiently high accuracy without the support of dedicated protruding probes, although it might use other protruding probes such as a Pitot probe for the measurement of the total pressure. In the past, researchers applied several techniques to obtain a synthetic sensor for AOA and AOS, for instance several studies exist on the application of Kalman Filter (KF) [2][3][4][5][6][7][8], other studies focus on the application of Neural Network (NN) [9][10][11], while the mathematical description of the problem resulting in an analytical estimator independent from the aircraft have been published in [12]. Another important research topic is the wind estimation, which is tighly related to the estimation of AOA/AOS [13].…”

Section: Introductionmentioning

confidence: 99%

Maneuver-Based Cross-Validation Approach for Angle-of-Attack Estimation

Brandl¹,

Battipede²

2021

14th WCCM-ECCOMAS Congress

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The estimation of the Angle of Attack (AOA) and Angle of Sideslip (AOS) is crucial for flight monitoring and control. However, a gap has been identified on the data selection technique for the class of estimators based on data-driven methods, such as the synthetic sensor based on Neural Network (NN). This paper proposes a Cross Validation (CV) technique applied on a manoeuver-based partitioning method to provide evidence that a given selection of data can lead to better estimation performance, with the final aim of providing a list of manoeuvers suitable for the training phase of the estimator. Results are shown using simulated data related to the CleanSky 2 project MIDAS.

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Aerodynamic angle estimation: comparison between numerical results and operative environment data

Cited by 17 publications

References 24 publications

Preliminary Design of a Model-Free Synthetic Sensor for Aerodynamic Angle Estimation for Commercial Aviation

Preliminary Design of a Model-Free Synthetic Sensor for Aerodynamic Angle Estimation for Commercial Aviation

An Innovative Angle of Attack Virtual Sensor for Physical-Analytical Redundant Measurement System Applicable to Commercial Aircraft

Maneuver-Based Cross-Validation Approach for Angle-of-Attack Estimation

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