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Nonlinear parameter estimation of airship using modular neural network

Published online by Cambridge University Press:  29 October 2019

S. Agrawal Open the ORCID record for S. Agrawal [Opens in a new window] ,
D. Gobiha  and
N.K. Sinha Open the ORCID record for N.K. Sinha [Opens in a new window]
S. Agrawal*
Affiliation:
[email protected] Flight Dynamics and Control Lab Department of Aerospace EngineeringIndian Institute of Technology Madras Chennai [email protected]
D. Gobiha
Affiliation:
[email protected] Flight Dynamics and Control Lab Department of Aerospace EngineeringIndian Institute of Technology Madras Chennai [email protected]
N.K. Sinha
Affiliation:
[email protected] Flight Dynamics and Control Lab Department of Aerospace EngineeringIndian Institute of Technology Madras Chennai [email protected]
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Abstract

The prime focus of this work is to estimate stability and control derivatives of an airship in a completely nonlinear environment. A complete six degrees of freedom airship model has its aerodynamic model as nonlinear functions of angle of attack. Estimating the parameters of aerodynamic model in a nonlinear environment is challenging as it demands an exhaustive dataset that could cover the entire regime of operation of airship. In this work, data generation is achieved by simulating the mathematical model of airship for different trim conditions obtained from continuation analysis. The mathematical model is simulated using predicted parameter values obtained using DATCOM methodology. A modular neural network is then trained using back-propagation and Adam optimisation algorithm for each of the aerodynamic coefficients separately. The estimated nonlinear airship parameters are found to be consistent with the DATCOM parameter values which were used for open-loop simulation. This validates the proposed methodology and could be extended to estimate airship parameters from real flight data.

Keywords

Parameter estimationNeural networkAirship
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1273 , March 2020 , pp. 409 - 428
Copyright
© Royal Aeronautical Society 2019 

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References

REFERENCES

Hamel, P.G. and Jategaonkar, R.V. “Evolution of flight vehicle system identification,” Journal of Aircraft, 1996, 33, (1), pp 9–28.CrossRefGoogle Scholar
Jategaonkar, R.V. Flight Vehicle System Identification: A Time-Domain Methodology, Progress in Aeronautics and Astronautics, vol. 216, AIAA, 2015, Reston, VA.Google Scholar
Peyada, N.K., Sen, 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, pp 19–21.Google Scholar
Youssef, H. and Juang, J.C. “Estimation of aerodynamic coefficients using neural networks,” AIAA Flight Simulation and Technologies Conference, Monterey, CA, 1993, pp 242–245.CrossRefGoogle Scholar
Basappa, K. and Jategaonkar, R. “Aspects of feed forward neural network modeling and its application to lateral-directional flight data,” DLR IB-111-95a30, 1995.Google Scholar
Raol, J. and Jategaonkar, R. “Aircraft parameter estimation using recurrent neural networks-A critical appraisal,” 20th Atmospheric Flight Mechanics Conference, Maryland, 1995, pp 119–128.CrossRefGoogle Scholar
Hornik, K., Stinchcombe, M. and White, H. “Multilayer feedforward networks are universal approximators,” Neural Networks, 1989, 2, (5), pp 359–366.CrossRefGoogle Scholar
Raisinghani, S., Ghosh, A. and Kalra, P. “Two new techniques for aircraft parameter estimation using neural networks,” The Aeronautical Journal, 1998, 102, (1011), pp 25–30.Google Scholar
Ghosh, A., Raisinghani, S. and Khubchandani, S. “Estimation of aircraft lateral-directional parameters using neural networks,” Journal of Aircraft, 1998, 35, (6), pp 876–881.CrossRefGoogle Scholar
Ghosh, A. and Raisinghani, S. “Parameter estimation from flight data of an unstable aircraft using neural networks,” Journal of Aircraft, 2002, 39, (5), pp 892–894.CrossRefGoogle Scholar
Peyada, N. and Ghosh, A. “Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss–Newton method,” The Aeronautical Journal, 2009, 113, (1142), pp 243–252.CrossRefGoogle Scholar
Kumar, R. and Ghosh, A. “Nonlinear longitudinal aerodynamic modeling using neural Gauss–Newton Method,” Journal of Aircraft, 2011, 48, (5), pp 1809–1813.CrossRefGoogle Scholar
Ding, Y., Feng, Q., Wang, T. and Fu, X. “A modular neural network architecture with concept,” Neurocomputing, 2014, 125, pp 3–6.CrossRefGoogle Scholar
Gobiha, D. and Sinha, N.K. “Autonomous maneuvering of a stratospheric airship,” Indian Control Conference, IEEE, Kanpur, India, 2018, pp 318–323.CrossRefGoogle Scholar
Tiwari, A., Vora, A. and Sinha, N.K. “Airship trim and stability analysis using bifurcation techniques,” 7th International Conference on Mechanical and Aerospace Engineering, IEEE, London, 2016, pp 471–475.CrossRefGoogle Scholar
Rana, V., Kumar, A., Sinha, N., Pal, A. and Sati, S. “Configuration analysis of stratospheric airship,” Symposium on Applied Aerodynamics and Design of Aerospace Vehicles, VSSC, Thiruvananthapuram, India, 2015.Google Scholar
Svozil, D., Kvasnicka, V. and Pospichal, J. “Introduction to multi-layer feed-forward neural networks,” Chemometrics and Intelligent Laboratory Systems, 1997, 39, (1), pp 43–62.CrossRefGoogle Scholar
Kingma, D.P. and Ba, J. “Adam: A method for stochastic optimization,” 3rd International Conference for Learning Representations, 2014.Google Scholar
Raol, J.R., Girija, G. and Singh, J. Modelling and Parameter Estimation of Dynamic Systems, 1st ed. The Institute of Engineering and Technology, 2004, London.CrossRefGoogle Scholar