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Parameter estimation of aircraft using extreme learning machine and Gauss-Newton algorithm

Published online by Cambridge University Press:  01 October 2019

H. O. Verma Open the ORCID record for H. O. Verma [Opens in a new window]  and
N. K. Peyada
H. O. Verma
Affiliation:
Department of Aerospace Engineering, Indian Institute of Technology Kharagpur, Indiahomverma@aero.iitkgp.ernet.in; homverma@gmail.com; nkpeyada@aero.iitkgp.ac.in
N. K. Peyada
Affiliation:
Department of Aerospace Engineering, Indian Institute of Technology Kharagpur, Indiahomverma@aero.iitkgp.ernet.in; homverma@gmail.com; nkpeyada@aero.iitkgp.ac.in
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Abstract

The research paper addresses the problem of estimating aerodynamic parameters using a Gauss-Newton-based optimisation method. The process of the optimisation method lies on the principle of minimising the residual error between the measured and simulated responses of the system. Usually, the simulated response is obtained by integrating the dynamic equations of the system, which is found to be susceptible to the initial values, and the integration method. With the advent of the feedforward neural network, the data-driven regression methods have been widely used for identification of the system. Among them, a variant of feedforward neural network, extreme learning machine, which has proven the performance in terms of computational cost, generalisation, and so forth, has been addressed to predict the responses in the present study. The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters. Furthermore, the estimates have been validated with the values of the classical estimation methods, such as the equation-error and filter-error methods. The sample standard deviations of the estimates demonstrate the effectiveness of the proposed method. Lastly, the proof-of-match exercise has been conducted with the other set of flight data to validate the estimated parameters.

Keywords

Non-linear modellingExtreme Learning Machine NetworkGauss-Newton MethodParameter Estimation
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1272 , February 2020 , pp. 271 - 295
Copyright
© Royal Aeronautical Society 2019 

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References

REFERENCES

Waszk, M.R. and Schmidt, D.K. Flight dynamics of aero-elastic vehicle, Journal of Aircraft, 1988, 25, (6), pp 563–571. doi:10.2514/3.45623.CrossRefGoogle Scholar
Etkin, B. Dynamic of Flight Stability and Control, John Wiley and Sons, 1982, New York, US.Google Scholar
Maine, R.E. and Iliff, K.W. Application of Parameter Estimation to Aircraft Stability and Control – The Output Error Approach. NASA RP 1168, January 1986.CrossRefGoogle Scholar
Raol, R., Jitendra, G.G. and Singh, J. Modelling and Parameter Estimation of Dynamic System, IET, 2004, London, UK.CrossRefGoogle Scholar
Jategaonkar, R.V. Flight Vehicle System Identification: A Time Domain Methodology, 1st ed, vol. 216, Progress in Astronautics and Aeronautics, AIAA, 2006, Reston, VA, US. doi:10.2514/4.866852.CrossRefGoogle Scholar
Klein, V. and Morelli, E. Aircraft System Identification: Theory and Practice, AIAA Education Series, AIAA, 2006, Reston, VA, US.CrossRefGoogle Scholar
Jategaonkar, R.V. and Plaetschke, E. Identification of moderately nonlinear flight mechanics systems with additive process and measurement noise, Journal of Guidance, Control, and Dynamics, 1990, 13, (2), pp 277–285. doi:10.2514/3.20547.CrossRefGoogle Scholar
Chowdhary, G. and Jategaonkar, R.V. Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter, AIAA Atmospheric Flight Mechanics Conference, Keystone, CO, US, 2006. doi:10.2514/6.2006-6146.CrossRefGoogle Scholar
Hornik, K. Approximation capabilities of multilayer feed-forward networks, Neural Network, 1991, 4, (2), pp 251–257. doi:10.1016/0893-6080(91)90009-T.CrossRefGoogle Scholar
Rumelhart, D.E. Hinton, G.E. and Williams, R.J., Learning representations by back propagation errors, Nature, 1986, 323, pp 533–536. doi:10.1038/323533a0.CrossRefGoogle Scholar
Haykin, S. Neural Networks and Learning Machines, 3rd ed, Prentice-Hall, 2009, Englewood Cliffs, NJ, US.Google Scholar
Pratihar, D.K. Soft Computing: Fundamentals and Applications, Narosa Publishing House, 2008, New Delhi, India.Google Scholar
Linse, D.J. and Stengel, R. Identification of aerodynamic coefficients using computational neural networks, Journal of Guidance, Control, and Dynamics, 1993, 16, (6), pp 1018–1025. doi:10.2514/3.21122.CrossRefGoogle Scholar
Raisinghani, S.C., Ghosh, A.K. and Kalra, P.K. Two new techniques for aircraft parameter estimation using neural networks, Aeronautical Journal, 1998, 102, (1011), pp 25–30. doi:10.1017/S0001924000065702.Google Scholar
Singh, S. and Ghosh, A.K. Estimation of lateral-directional parameters using neural network based modified delta method, Aeronautical Journal, 2007, 111, (1124), pp 659–667. doi:10.1017/S0001924000004838.CrossRefGoogle Scholar
Garhwal, R., Halder, A. and Sinha, M. Sensitivity analysis using neural network for estimating aircraft stability and control derivatives, IEEE International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia, 2007. doi:10.1109/ICIAS.2007.4658380.CrossRefGoogle Scholar
Das, S., Kutteri, R.A., Sinha, M. and Jategaonkar, R.V. Neural partial differential method for extracting aerodynamic derivatives from flight data, Journal of Guidance, Control, and Dynamics, 2010, 33, (2), pp 376–384. doi:10.2514/1.46053.CrossRefGoogle Scholar
Peyada, N.K. and Ghosh, A.K. Aircraft parameter estimation using new filtering technique based on neural network and Gauss-Newton method, Aeronautical Journal, 2009, 113, (1142), pp 243–252. doi:10.1017/S0001924000002918.CrossRefGoogle Scholar
Kumar, R. and Ghosh, A.K. Nonlinear longitudinal aerodynamic modeling using neural Gauss-Newton method, Journal of Aircraft, 2011, 48, (5), pp 1809–1812. doi:10.2514/1.C031253.CrossRefGoogle Scholar
Saderla, S., Dhayalan, R. and Ghosh, A.K. Non-linear aerodynamic modelling of unmanned cropped delta configuration from experimental data, Aeronautical Journal, 2017, 121, (1237), pp 320–340. doi:10.1017/aer.2016.124.CrossRefGoogle Scholar
Kumar, R., Ganguli, R., Omkar, S.N. and Kumar, M.V. Rotorcraft parameter identification from real time flight data, Journal of Aircraft, 2008, 45, (1), pp 333–341. doi:10.2514/1.32024.CrossRefGoogle Scholar
Sanwale, J. and Singh, D.J. Aerodynamic parameters estimation using radial basis function neural partial differentiation method, Defence Science Journal, 2018; 68, (3), pp 241–250. doi:10.14429/dsj.68.11843.CrossRefGoogle Scholar
Kumar, A. and Ghosh, A.K. ANFIS-delta method for aerodynamic parameter estimation using flight data, Journal of Aerospace Engineering, 2019, 233, (8), pp 3016–3032. doi:10.1177/0954410018791621.Google Scholar
Roy, A.G. and Peyada, N.K. Aircraft parameter estimation using hybrid neuro fuzzy and artificial bee colony optimization (HNFABC) algorithm, Journal of Aerospace Science and Technology, 2017, 71, pp 772–782. doi:10.1016/j.ast.2017.10.030.Google Scholar
Huang, G.B., Zhu, Q.Y. and Siew, C.K. Extreme learning machine: a new learning scheme of feed forward neural networks, IEEE International Joint Conference on Neural Networks, 2004, Budapest, Hungary. doi:10.1109/IJCNN.2004.1380068.CrossRefGoogle Scholar
Huang, G.B., Zhu, Q.Y. and Siew, C.K. Extreme learning machine: theory and applications, Neurocomputing, 2006, 70, (1–3), pp 489–501. doi:10.1016/j.neucom.2005.12.126.CrossRefGoogle Scholar
Sun, Z.L., Choi, T.M., Au, K.F. and Yu, Y. Sales forecasting using extreme learning machine with applications in fashion retailing, Decision Support Systems, 2008, 46, (1), pp 411–419. doi:10.101 6/j.dss.2008.07.009.CrossRefGoogle Scholar
Chacko, B.P., Krishnan, V.R.V., Raju, G. and Anto, P.B. Handwritten character recognition using wavelet energy and extreme learning machine, International Journal of Machine Learning and Cybernetics, 2012, 3, (2), pp 149–161. doi:10.1007/s13042-010049-5.CrossRefGoogle Scholar
Zhao, Z., Li, P. and Xu, X. Forecasting model of coal mine water inrush based on extreme learning machine, Applied Mathematics and Information Sciences, 2013, 7, (3), pp 1243–1250. doi:10.12785/amis/070349.CrossRefGoogle Scholar
Pal, M. Extreme learning machine-based land cover classification, International Journal of Remote Sensing, 2009, 30, (14), pp 3835–3841. doi:10.1080/01431160902788636.CrossRefGoogle Scholar
Zong, W.W. and Huang, G.B. Face recognition based on extreme learning machine, Neurocomputing, 2011, 74, (16), pp 2541–2551. doi:10.1016/j.neucom.2010.12.041.CrossRefGoogle Scholar
Pamadi, B.N. Performance, Stability, Dynamics and Control of Airplanes, AIAA Education Series, 1998, Virginia.Google Scholar
Sola, J. and Sevilla, J. Importance of input data normalization for the application of neural networks to complex industrial problems, IEEE Transactions on Nuclear Science, 1997, 44, (3), pp 1464–1468. doi:10.1109/23.589532.CrossRefGoogle Scholar
Peyada, N.K. Parameter Estimation from Flight Data Using Feed Forward Neural Networks. PhD Thesis, IIT Kanpur, India, 2009.Google Scholar
Sahin, M. Comparison of modelling ANN and ELM to estimate solar radiation over Turkey using NOAA satellite data, International Journal of Remote Sensing, 2013, 34, (21), pp 7508–7533. doi:10.1080/01431161.2013.822597.CrossRefGoogle Scholar
Filippone, A. Flight Performance of Fixed and Rotary Wing Aircraft, Elsevier, 2006, Oxford, UK.CrossRefGoogle Scholar
Verma, H.O., Peyada, N.K. Parameter estimation of stable and unstable aircraft using extreme learning machine, AIAA Atmospheric Flight Mechanics Conference 2018. doi:10.2514/6.2018-0526.CrossRefGoogle Scholar
Rangaranjan, R. and Vishwanathan, S. Wind Tunnel Test Results on a 1/5 Scale HANSA Model. NAL TR-01 1997.Google Scholar
Morelli, E.A. Practical aspects of the equation error method for aircraft parameter estimation, AIAA Atmospheric Flight Mechanics Conference, Keystone, CO, US, 2006. doi:10.2514/6.2006-6144.CrossRefGoogle Scholar