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A GPR Based Novel Approach for Aerodynamic Parameter Estimation from Flight Data

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In this paper, a novel method based on Gaussian process regression(GPR) is proposed for the aerodynamic parameter estimation from the flight data. The new method GPR-Delta is an extension of the feed-forward neural network (FFNN) based Delta method. The GPR-Delta augments the philosophies of kernel-based nonparametric probabilistic models in the Delta method by replacing the FFNN. Efficacy of the proposed algorithm is examined by estimating the aerodynamic parameters using flight test data of two different categories of the aircraft. GPR-Delta estimated parameters are compared with the wind tunnel, Delta and Filter error method estimated aerodynamic parameters. Comparison results establish the GPR-Delta as a viable alternative method to this problem.
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Aerodynamic Parameter Estimation; Gaussian Process Regression; Delta Method; Flight Data; GPR-Delta

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R. V. Jategaonkar, Flight Vehicle System Identification: A Time Domain Methodology, (AIAA Education Series, AIAA, Reston, VA, 2006).

Morelli Eugene A, Real-Time Aerodynamic Parameter Estimation without Air Flow Angle Measurements, Journal of Aircraft, Vol. 49, No. 4, July –August (2012).

Peyada, N. K., Sinha A. and Ghosh, A. K., Aerodynamic Characterization of HANSA-3 aircraft using Equation Error, Maximum Likelihood, and Filter Error Methods, Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong (2008).

Chowdhary, G., Jategaonkar R. V., Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter. Aerospace Science and Technology, Vol. 14 pp. 106-117, Issue 2 (2010).

Sadrela S., Dhayalan R. and Ghosh, A. K., Longitudinal parameter estimation from real flight data of unmanned cropped delta flat plate configuration, International Journal of Intelligent Unmanned Systems, Vol. 4 pp. 2 – 22 (2016).

J. Grauer, E.Morelli, A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence, AIAA Atmospheric Flight Mechanics Conference; Dallas, TX; the United States 22-26 Jun. (2015).

Morelli, E., Efficient Global Aerodynamic Modeling from Flight Data, 50th Aerospace Sciences Meeting, AIAA, Issue- Jan, pp. 1-26, (2012).

Ghosh, A. K. Raisinghani, S. C. and Khubchandani Sunil, Estimation of Aircraft Lateral-Directional Parameters Using Neural Networks, Journal of Aircraft, Vol. 35, No. 6, November –December (1998).

Singh S. and Ghosh, A. K., Estimation of Lateral-Directional parameters Using Neural Networks Based Modified Delta Method, The Aeronautical Journal, Vol. 111, Issue -3150, pp. 659-667, (2007).

Peyada, N. K., and Ghosh, A. K., Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method, The Aeronautical Journal, Vol. 113 No. 1142, pp. 243-252, (2009).

D. Ignatyev, A Khrabrov, Alexander N, Neural network modeling of unsteady aerodynamic characteristics at high angles of attack, Aerospace Science and Technology, Vol. 41, pp. 106-115, (2015).

Babuska, R., Neuro-fuzzy methods for modeling and identification, Recent Advances in Intelligent Paradigms and Applications, pages 161–186, Springer-Verlag, Heidelberg, (2002).

Raol R., Jitendra, G. Girija and Singh, Jatinder, Modelling and Parameter Estimation of Dynamic Systems, (IET, London, United Kingdom, (2004).

Nelles, Oliver. Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer Science & Business Media, (2013).

V. Dongare, M. Mohamed, Lateral-Directional Aerodynamics Parameter Estimation using Neural Partial Differentiation, International Conference on Cognitive Computing and Information Processing CCIP, (2015).

J. S. Roger, C. T. Sun, Neuro-Fuzzy Modeling and Control, IEEE Trans. on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, (1993).

Boëly, N. and Botez, R. M., New approach for the identification and validation of a nonlinear F/A- 18 model by use of neural networks, IEEE Transactions on Neural Networks, 21, (11), pp 1759-1765(2010).

De Jesus Mota, S. and Botez, R. M., New helicopter model identification method based on a neural network optimization algorithm and flight test data Aeronautical J., 115, (1167), pp 295-314 (2011).

Chand, A. N., Kawanishi, M. and Narikiyo, T., Parameter estimation for the pitching dynamics of a flapping-wing flying robot. In Advanced Intelligent Mechatronics (AIM), IEEE International Conference on pp. 1552-1558, (2015).

Guo, Mengwu, and Jan S. Hesthaven. Reduced order modeling for nonlinear structural analysis using Gaussian process regression. No. EPFL-ARTICLE-232957. Elsevier, (2017).

Wass, J., Thrane, J., Piels, M., Jones, R., & Zibar, D., Gaussian process regression for WDM system performance prediction. In Optical Fiber Communications Conference and Exhibition (OFC), (pp. 1-3). IEEE (2017).

Nguyen-Tuong, D., Seeger, M. and Peters, J., Model learning with local Gaussian process regression. Advanced Robotics, 23(15), pp.2015-2034 (2009).

Ebden, M. Gaussian Processes for Regression, A Quick Introduction. The Website of Robotics Research Group in the Department of Engineering Science, University of Oxford: Oxford (2008).

Koistinen, O. P., Dagbjartsdóttir, F. B., Ásgeirsson, V., Vehtari, A. and Jónsson, H., 2017. Nudged elastic band calculations accelerated with Gaussian process regression. arXiv preprint arXiv:1706.04606.

Wang, T. D., Chuang, S. J., Fyfe, C. Comparing Gaussian processes and artificial neural networks for forecasting. In Proc of 9th Joint Conf on Information Sciences. Taiwan (pp. 1-4) (2006).

Alias, M., Mohd Rafie, A., Wiriadidjaja, S., Two Dimensional Numerical Study of Aerodynamic Characteristic for Rotating Cylinder at High Reynolds Number, (2016) International Review of Aerospace Engineering (IREASE), 9 (6), pp. 208-215.

Mehta, R., Aerodynamic Design of Payload Fairing of Satellite Launch Vehicle, (2015) International Review of Aerospace Engineering (IREASE), 8 (5), pp. 167-173.

Grillo, C., Montano, F., An EKF Based Method for Path Following in Turbulent Air, (2017) International Review of Aerospace Engineering (IREASE), 10 (1), pp. 1-6.


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