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Books > Professional & Technical > Transport technology > Aerospace & aviation technology
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Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation (Paperback, 1st ed. 2021)
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Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation (Paperback, 1st ed. 2021)
Series: SpringerBriefs in Applied Sciences and Technology
Expected to ship within 10 - 15 working days
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This book presents neural partial differentiation as an estimation
algorithm for extracting aerodynamic derivatives from flight data.
It discusses neural modeling of the aircraft system. The neural
partial differentiation approach discussed in the book helps
estimate parameters with their statistical information from the
noisy data. Moreover, this method avoids the need for prior
information about the aircraft model parameters. The objective of
the book is to extend the use of the neural partial differentiation
method to the multi-input multi-output aircraft system for the
online estimation of aircraft parameters from an established neural
model. This approach will be relevant for the design of an adaptive
flight control system. The book also discusses the estimation of
aerodynamic derivatives of rigid and flexible aircraft which are
treated separately. The longitudinal and lateral-directional
derivatives of aircraft are estimated from flight data. Besides the
aerodynamic derivatives, mode shape parameters of flexible aircraft
are also identified in the book as part of identification for the
state space aircraft model. Since the detailed description of the
approach is illustrated through the block diagram and their results
are presented in tabular form with figures of parameters converge
to their estimates, the contents of this book are intended for
readers who want to pursue a postgraduate and doctoral degree in
science and engineering. This book is useful for practicing
scientists, engineers, and teachers in the field of aerospace
engineering.
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