Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation
(Sprache: Englisch)
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...
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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.Inhaltsverzeichnis zu „Aircraft Aerodynamic Parameter Estimation from Flight Data Using Neural Partial Differentiation “
Aircraft System Identification.- Neural Modeling and Parameter Estimation.- Identification of Aircraft Longitudinal Derivatives.- Identification of Aircraft Lateral-directional Derivatives.- Identification of a Flexible Aircraft Derivatives.- Conclusions and Future Work.- Appendix A: Neural Network Based Solution of Ordinary Differential Equation.- Appendix B: Output Error Method.
Autoren-Porträt von Majeed Mohamed, Vikalp Dongare
Majeed Mohamed did his degree in Instrumentation and Control Engineering and M.Tech. in Control Systems from IIT Delhi in 2002 and completed his Ph.D. in Flight Dynamics and Control from IIT Delhi in 2012. He is presently working as Principal Scientist in Flight Mechanics and Control Division at National Aerospace Laboratories (NAL) Bangalore and has worked with CSIR for 21 years. Dr. Majeed is the recipient of a research fellowship award from Nanyang Technological University (NTU), Singapore, for his postdoctoral work in 2016 at the Air Traffic Management Research Institute (ATMRI), Singapore. He has authored the book 'Aircraft System Identification and Control' in 2013. Dr. Majeed has published 17 international journal papers and 20 international papers in IFAC and IEEE conferences. He has guided over ten M.Tech. students of flight dynamics and control. He is also Associate Professor of the Academy of Scientific and Innovative Research (AcSIR), New Delhi, India.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.
Bibliographische Angaben
- Autoren: Majeed Mohamed , Vikalp Dongare
- 2021, 1st ed. 2021, XI, 66 Seiten, 66 farbige Abbildungen, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 9811601038
- ISBN-13: 9789811601033
Sprache:
Englisch
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