Verification in Relevant Environment of a Physics-Based Synthetic Sensor for Flow Angle Estimation

Lerro, Angelo; Gili, Piero; Pisani, Marco

doi:10.3390/electronics11010165

Cited by 3 publications

(1 citation statement)

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“…Lerro A. et al [5] in their paper aimed to describe the verification in a relevant environment of a physics-based approach using a dedicated technological demonstrator. The flow angle synthetic solution is based on a model-free, or physics-based, scheme and, therefore, it is applicable to any flying body.…”

Section: Review Papersmentioning

confidence: 99%

Unmanned Aircraft Systems with Autonomous Navigation

Papa

2023

Electronics

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Section: Review Papersmentioning

confidence: 99%

Unmanned Aircraft Systems with Autonomous Navigation

Papa

2023

Electronics

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Assessment of global and local neural network’s performance for model-free estimation of flow angles

Lerro

Pasquale

2023

Aeronaut. j.

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A synthetic flow angle sensor, able to estimate angle-of-attack and angle-of-sideslip, can exploit different methods to solve a set of equations modelling data fusion from other onboard systems. In operative scenarios, measurements used for data fusion are characterised by several uncertainties that would significantly affect the synthetic sensor performance. The off-line use of neural networks is not a novelty to model deterministic synthetic flow angle sensors and to mitigate issues arising from real flight applications. A common practice is to train the neural network with corrupted data that are representative of uncertainties of the current application. However, this approach requires accurate tuning on the target aircraft and extensive flight test campaigns, therefore, making the neural network tightly dependent on the specific aircraft. In order to overcome latter issues, this work proposes the use of neural networks to solve a model-free scheme, derived from classical flight mechanics, that is independent from the target aircraft, flight regime and avionics. It is crucial to make use of a training dataset that is not related to any specific aircraft or avionics to preserve the generality of the scheme. Under these circumstances, global and local neural networks are herein compared with an iterative method to assess the neural capabilities to generalise the proposed model-free solver. The final objective of the present work, in fact, is to select the neural technique that can enable a flow angle synthetic sensor to be used on board any flying body at any flight regime without any further training sessions.

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Angle-of-Attack Estimation for General Aviation Aircraft

et al. 2023

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The angle of attack is one of the most important flight parameters. In the framework of the present study, a flight data recording method was designed to analyze the Valasek angle-of-attack estimation method and investigate its applicability for general aviation aircraft. This was performed using two devices characterized by substantially different characteristics. The test flight, and the ground test, i.e., a flight simulator experiment, were conducted. Two flight regimes were analyzed: (a) steady climb and descent with low values of angle of attack, (b) approach to stall with idle power with an increase of the angle of attack to the critical value. A satisfactory angle of attack estimate was obtained for the steady climb and descent regime, while the approach to stall estimate was less accurate but still indicative and considered useful for the pilot. The results indicate that less expensive synthetic sensors may provide acceptable results compared to high-quality certified equipment. A proposed modification of the estimation method enables simplification of the required equipment, while offering important information to the pilot.

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Verification in Relevant Environment of a Physics-Based Synthetic Sensor for Flow Angle Estimation

Cited by 3 publications

References 31 publications

Unmanned Aircraft Systems with Autonomous Navigation

Unmanned Aircraft Systems with Autonomous Navigation

Assessment of global and local neural network’s performance for model-free estimation of flow angles

Angle-of-Attack Estimation for General Aviation Aircraft

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