Generalization of ANN-based aircraft dynamics identification techniques into the entire flight envelope

In this paper, an approach has been proposed in order to extend the applicability of artificial neural network (ANN) techniques for flight dynamics identification into the entire flight envelope. In general, the aircraft flight dynamics is a nonlinear and coupled system whose modeling by ANNs is only possible to a limited degree around an operational point. Therefore, it cannot be expected that an ANN trained at a specific Mach and altitude will have satisfactory results in various flight conditions. Most recent studies on ANN-based identification and modeling of aircraft dynamics have been carried out primarily at specific Mach and altitudes. In this study, by introducing a new approach based on a multi-input multi-output ANN with multidimensional output, the identification process has been successfully extended into the entire flight envelope. The results show that the proposed network, trained with experimental flight test data, is able to model the dynamical behavior of a highly maneuverable aircraft with acceptable accuracy in all of its flight conditions.

[1]  Seán McLoone.  Neural network identification: a survey of gradient based methods , 1998 .

[2]  Detlef Rohlf,et al.  Global Model Approach for X-31 VECTOR System Identification , 2005 .

[3]  Aydogan Savran,et al.  Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks. , 2006, ISA transactions.

[4]  J. Juang.  Applied system identification , 1994 .

[5]  Mark B. Tischler,et al.  Frequency-response identification of XV-15 tilt-rotor aircraft dynamics , 1983 .

[6]  Matthew Garratt,et al.  Neural Network Based System Identification for Autonomous Flight of an Eagle Helicopter , 2008 .

[7]  Randall S. Sexton,et al.  Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing , 1999, Eur. J. Oper. Res..

[8]  Mark B. Tischler,et al.  Aircraft and Rotorcraft System Identification: Engineering Methods with Flight-Test Examples , 2006 .

[9]  Alireza Roudbari,et al.  An evolutionary optimizing approach to neural network architecture for improving identification and modeling of aircraft nonlinear dynamics , 2014 .

[10]  Celso Braga de Mendonca.  Flight Vehicle System Identification: A Time-Domain MethodologyJategaonkarR. V., Progress in Aeronautics and Astronautics, 2nd ed., AIAA, Reston, VA, 2015, 648 pp., $119.95. , 2016 .

[11]  D. C. Dugan,et al.  Frequency-Domain Identification of XV-15 Tilt-Rotor Aircraft Dynamics in Hovering Flight , 1985 .

[12]  Frank L. Lewis,et al.  Aircraft Control and Simulation , 1992 .

[13]  M. N. Beaulieu,et al.  Identification of structural surfaces' positions on an F/A-18 using the subspace identification method from flight flutter tests , 2007 .

[14]  Duc Truong Pham,et al.  Training Elman and Jordan networks for system identification using genetic algorithms , 1999, Artif. Intell. Eng..

[15]  O. Nelles.  Nonlinear System Identification , 2001 .

[16]  Ute Hoffmann.  Aircraft System Identification Theory And Practice , 2016 .

[17]  Anthony J. Calise,et al.  Analysis of Adaptive Neural Networks for Helicopter Flight Control , 1997 .

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[20]  Ruxandra Botez,et al.  Identification of a non-linear F/A-18 model by the use of fuzzy logic and neural network methods , 2011 .

[21]  Peter H. Zipfel,et al.  Modeling and Simulation of Aerospace Vehicle Dynamics , 2001 .

[22]  Alireza Roudbari,et al.  Intelligent modeling and identification of aircraft nonlinear flight , 2014 .

[23]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[24]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[25]  Bernard Mettler,et al.  System identification modeling of a small-scale unmanned rotorcraft for flight control design , 2002 .

[26]  David G. Ward,et al.  System Identification for Retrofit Reconfigurable Control of an F/A-18 Aircraft , 2005 .

[27]  S.N. Omkar,et al.  Nonlinear lateral command control using neural network for F-16 aircraft , 2005, Proceedings of the 2005, American Control Conference, 2005..

[28]  Randall S. Sexton,et al.  Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation , 1998, Decis. Support Syst..

[29]  Ranjan Ganguli,et al.  Identification of Helicopter Dynamics Using Recurrent Neural Networks and Flight Data , 2006 .

[30]  Jan Albert Mulder,et al.  Flight Test Results of Five Input Signals for Aircraft Parameter Identification , 1982 .

[31]  M. Ananthasayanam,et al.  Parameter Estimation of a Flight Vehicle Using MMLE/BFGS Estimator Under Limited Measurements , 2002 .

[32]  Feng Yi,et al.  Flight Dynamics Identification of a Helicopter in Hovering Based on Flight Data , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[33]  Ayman Hamdy Kassem.  Efficient Neural Network Modeling for Flight and Space Dynamics Simulation , 2011 .

[34]  Michael I. Jordan.  Serial Order: A Parallel Distributed Processing Approach , 1997 .

[35]  Eugene A. Morelli,et al.  Application of System Identification to Aircraft at NASA Langley Research Center , 2005 .

[36]  R. V. Jategaonkar,et al.  Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter , 2006 .

[37]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[38]  Ravindra Jategaonkar,et al.  Aerodynamic Modeling and System Identification from Flight Data , 2005, Journal of Aerospace Sciences and Technologies.

[39]  Ruxandra Mihaela Botez,et al.  New Approach for the Identification and Validation of a Nonlinear F/A-18 Model by Use of Neural Networks , 2010, IEEE Transactions on Neural Networks.

[40]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[41]  Ruxandra Botez,et al.  New helicopter model identification method based on flight test data , 2011 .

[42]  Randall S. Sexton,et al.  Simultaneous optimization of neural network function and architecture algorithm , 2004, Decis. Support Syst..