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Estimation of nonlinear airship parameters using modular neural network
Published in Institute of Electrical and Electronics Engineers Inc.
2019
Pages: 45 - 50
Abstract
The prime objective of this work is to estimate stability and control derivatives of an airship. The complete, nonlinear mathematical model of aerial vehicles has its aero model as a nonlinear function of angle of attack. This along with the necessity for an exhaustive dataset complicates the estimation procedure. In this work, data are generated by simulating the mathematical model of airship for different trim conditions obtained from continuation analysis. A modular neural network is then trained using back-propagation and Adam optimization algorithm for each aerodynamic coefficient separately. The estimated nonlinear airship parameters are found to be consistent with the DATCOM parameters which were used for open-loop simulation in data generation phase. This validates the proposed methodology and could be extended to estimate airship parameters from real flight data. © 2019 IEEE.
About the journal
JournalData powered by Typeset2019 5th Indian Control Conference, ICC 2019 - Proceedings
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo
Concepts (16)
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    Airships
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    Angle of attack
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    Antennas
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    Backpropagation algorithms
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    Flight control systems
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    Functions
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    Nonlinear analysis
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    Aerodynamic coefficients
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    Continuation analysis
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    Estimation procedures
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    MODULAR NEURAL NETWORKS
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    NONLINEAR MATHEMATICAL MODEL
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    OPEN-LOOP SIMULATIONS
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    Optimization algorithms
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    Stability and control
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    Parameter estimation