Overview
- Contains detailed mathematical steps for the readers to learn the approach of neural partial differentiation to estimate parameters of the aircraft dynamic system
- Outlines aircraft modeling and parameter estimation in detail with the help of block diagram for the approach of Neural Partial Differentiation
- Describes flexible aircraft parameter estimation
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
About the authors
Vikalp Dongare did his degree in Avionics System and Engineering in 2012 from Aeronautical Society of India, New Delhi, and M.Tech. in Aeronautical Engineering from Visvesvaraya Technological University, Bangalore, in 2015. He has completed an internship of one year from CSIR-National Aerospace Laboratories Bangalore and has several journal and conference proceedings publications. He is a life member of the Aeronautical Society of India. Vikalp is presently working as a Data Scientist in a multinational corporation to build advanced analytical models for aviation and healthcare businesses. He has experience in making big data analytics and machine learning models.
Bibliographic Information
Book Title: Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation
Authors: Majeed Mohamed, Vikalp Dongare
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-981-16-0104-0
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
Softcover ISBN: 978-981-16-0103-3Published: 24 February 2021
eBook ISBN: 978-981-16-0104-0Published: 23 February 2021
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: XI, 66
Number of Illustrations: 32 illustrations in colour
Topics: Aerospace Technology and Astronautics, Automotive Engineering, Mathematical Modeling and Industrial Mathematics