Development of a Tool for Pilot Induced Oscillations (PIO) Testing in Flight Simulator

Neves, Gabriel Daher de Alencar; Bruschi, Adriano; Bidinotto, Jorge Henrique

doi:10.26678/abcm.cobem2021.cob2021-0036

Cited by 3 publications

(2 citation statements)

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“…The software SynTask was developed in the Human Factors Laboratory at the University of São Paulo and registered as number BR512022002377-6 [18], whose development is described in [19]. The software was developed in MATLAB® and gives the interface of an Artificial Horizon with a red line, used as the task that the pilot should perform, following this line using a joystick.…”

Section: 1syntask Softwarementioning

confidence: 99%

Artificial Neural Networks for PIO Events Classification Comparing Different Data Collection Procedures

Bruschi,

Drewiacki,

Bidinotto

2024

Preprint

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This work evaluates the accuracy and reliability of PIO test classification using the PIO Rating Scale and proposes using an automatic tool for this evaluation based on test data to eliminate the subjectivity inherent to the application of rating scales. Two test procedures (Discrete Synthetic Task and Pitch Capture) are executed in a flight simulator, using aircraft dynamic models with different PIO proneness and experienced flight test pilots. The results show a significant effect of subjectivity in pilot rating and various reliability for different test procedures. This data is used to build an Artificial Neural Network (ANN) proposed to classify the executions using the PIO Rating Scale. The ANN presented low computational cost and 97.1% accuracy when using data extracted from the Pitch Capture test procedure.

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Section: 1syntask Softwarementioning

confidence: 99%

Artificial Neural Networks for PIO Events Classification Comparing Different Data Collection Procedures

Bruschi,

Drewiacki,

Bidinotto

2024

Preprint

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“…Este trabalho utilizou como veículo condutor dos ensaios o ambiente de simulação provido pelo Departamento de Engenharia Aeronáutica da Escola de Engenharia de São Carlos EESC -USP. Este ambiente é fruto de trabalhos anteriores de alunos no desenvolvimento e concepção de um ambiente para prática e obtenção dos dados a serem utilizados nos trabalhos dos grupos de pesquisa do Departamento de Engenharia Aeronáutica (Moura, Alegre, Bidinotto, Belo, 2018;Bidinotto, Moura, Neves, 2022;Neves, Bruschi, Bidinotto, 2021;Moura 2018) Foi utilizado o modelo do Boeing 747 como planta da aeronave que será melhor descrito no capítulo Aeronave Modelo. O sistema visual empregado foi uma réplica de um sinótico de orientação de atitude comumente utilizados na aviação.…”

Section: Ambiente De Simulaçãounclassified

Classificação de eventos de PIO utilizando Redes Neurais Artificiais

Bruschi¹

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During the certification and the flight tests of a new airplane, the verification process to prove the low susceptibility at PIO is usually done for the experience of the test pilots, thus, the result and safety operation regarding PIO is intrinsic the assessment subjectivity, in other words, the certification process of PIO is directly related with the test pilots experiences. New airplanes tend to reduce stability margins, that is, increasing PIO tendencies, and full autonomous airplanes with capacity to perform a complete flight without crew, for this reason it is necessary the reduction of the subjectivity of PIO assessment for next generation of aircraft. This project covers the development of a neural network that will be able to classify the PIO oscillations in accordance with the PIOR Scale and to the subjective assessment of PIO classification.Neural networks assertiveness was higher for PIO classification than the result done by pilots, when the choice of inputs follow the guidelines established for this work.

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Artificial neural networks for PIO events classification comparing different data collection procedures

Bruschi,

Drewiacki,

Bidinotto

2024

J Braz. Soc. Mech. Sci. Eng.

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Development of a Tool for Pilot Induced Oscillations (PIO) Testing in Flight Simulator

Cited by 3 publications

References 0 publications

Artificial Neural Networks for PIO Events Classification Comparing Different Data Collection Procedures

Artificial Neural Networks for PIO Events Classification Comparing Different Data Collection Procedures

Classificação de eventos de PIO utilizando Redes Neurais Artificiais

Artificial neural networks for PIO events classification comparing different data collection procedures

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