Neural Partial Differentiation Based Nonlinear Parameter Estimation from Noisy Flight Data

This paper focuses on the application of neural partial differentiation (NPD) approach to estimate the longitudinal parameters of an aircraft HFB 320 from noisy flight data. By exciting both short period and phugoid modes of an aircraft with thrust variation, the aircraft system dynamics becomes hig...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1215; no. 1; pp. 12025 - 12035
Main Authors Mohamed, Majeed, Madhavan, G, Manikantan, R, Priya, PS Lal
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.05.2019
Subjects
Aircraft accidents & safety
Aircraft noise
Differentiation
Dynamical systems
Kalman filters
Nonlinear dynamics
Parameter estimation
Physics
Pitch (inclination)
System dynamics
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Summary:This paper focuses on the application of neural partial differentiation (NPD) approach to estimate the longitudinal parameters of an aircraft HFB 320 from noisy flight data. By exciting both short period and phugoid modes of an aircraft with thrust variation, the aircraft system dynamics becomes highly nonlinear and aerodynamic parameters appears nonlinear to the state trajectories of velocity, AOA, pitch rate and pitch angle. This paper highlights the application of NPD for such a class of nonlinear dynamics; previously it was used only for the estimation of parameter appearing linear to the states. The extracted the nonlinear longitudinal parameters of HFB 320 aircraft are compared with the parameters estimated by using adaptive Unscented Kalman Filter (UKF) approach. Finally, the estimation results are validated by comparing with flight data and the responses obtained from the estimates by adaptive UKF.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1215/1/012025