**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**31532

##### Advantages of Neural Network Based Air Data Estimation for Unmanned Aerial Vehicles

**Authors:**
Angelo Lerro,
Manuela Battipede,
Piero Gili,
Alberto Brandl

**Abstract:**

**Keywords:**
Neural network,
aerodynamic angles,
virtual sensor,
unmanned aerial vehicle,
air data system,
flight test.

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1130557

**References:**

[1] I. Samy, I. Postlethwaite, and D. W. Gu, Survey and application of sensor fault detection and isolation schemes, Control Eng. Pract., vol. 19, no. 7, pp. 658674, 2011.

[2] K. C. Wong, Aerospace industry opportunities in Australia-unmanned aerial vehicles (UAVs). Department of Aeronautical Engineering, University of Sydney, 2007.

[3] FAA Modernization and Reform Act (2012). H.R. 658, 112th Congress, 2nd Session.

[4] P. Freeman, P. Seiler, and G. J. Balas, Air data system fault modeling and detection, Control Eng. Pract., vol. 21, no. 10, pp. 12901301, 2013.

[5] J. Marzat, H. Piet-Lahanier, F. Damongeot, and E. Walter, Model-based fault diagnosis for aerospace systems: a survey, Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., vol. 226, no. 10, pp. 13291360, 2012.

[6] M. Oosterom, R. Babuska, Virtual Sensor for the Angle-of-Attack Signal in Small Commercial Aircraft, 2006 IEEE International Conference on Fuzzy Systems, pp. 1396-1403, Sept 2006.

[7] G. Hardier, C. Seren, P. Ezerzere, Model Based Techniques for Virtual Sensing of Longitudinal Flight Parameters, International Journal of Applied Mathematics and Computer Science, vol. 25, no. 1, pp. 23-28, Mar 2015.

[8] A. Lerro, M. Battipede, P. Gili, ”Sistema e procedimento di misura e valutazione di dati aria e inerziali”, TO2013A000601, 2013.

[9] M. Battipede, P. Gili, A. Lerro, S. Caselle, P. Gianardi, ”Development of Neural Networks for Air Data Estimation: Training of Neural Network Using Noise-Corrupted Data”, 3rd CEAS Air & Space Conference, 21st AIDAA Congress, ISBN: 9788896427187, 2011.

[10] M. Battipede, M. Cassaro, P. Gili, A. Lerro, ”Novel Neural Architecture for Air Data Angle Estimation”, In: L. Iliadis, H. Papadopoulos, C. Jayne (eds) Engineering Applications of Neural Networks, EANN 2013, Communications in Computer and Information Science, vol 383, pp. 313-322, Springer, Berlin, Heidelberg, DOI: 10.1007/978-3-642-41013-0 32, Sept 2013.

[11] P. Gili, M. Battipede, A. Lerro, ”Neural networks for air data estimation: test of neural network simulating real flight instruments”, In: C. Jayne, S. Yue, L. Iliadis (eds) Engineering Applications of Neural Networks, EANN 2012, Communications in Computer and Information Science, vol 311, Springer, Berlin, Heidelberg, ISBN:978-364232908-1, DOI: 10.1007/978-3-642-32909-8 29, Sept 2012.

[12] A. Lerro, M. Battipede, P. Gili, A. Brandl, ”Survey on a Neural Network for Non Linear Estimation of Aerodynamic Angles”, accepted but not yet presented for Intelligent Systems Conference 2017, London, 2017.

[13] J. G. Attali and G. Pags, Approximations of Functions by a Multilayer Perceptron: a New Approach, Neural Networks, vol. 10, no. 6, pp. 10691081, Aug. 1997.

[14] J. L. Castro and C. J. Mantas, Neural networks with a continuous squashing function in the output are universal approximators, vol. 13, pp. 561563, 2000.

[15] C. M. Bishop, Neural networks for pattern recognition, J. Am. Stat. Assoc., vol. 92, 1995.

[16] K. Levenberg, A method for the solution of certain non-linear problems in least squares. Quarterly Journal of Applied Mathematics I I (2), 164-168, 1944.

[17] D. W. Marquardt, An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society of Industrial and Applied Mathematics 11 (2), 431-441, 1963.

[18] M. Riedmiller and H. Braun, A direct adaptive method for faster backpropagation learning: The RPROP algorithm, in IEEE International Conference on Neural Networks - Conference Proceedings, 1993.

[19] . Kisi and E. Uncuoglu, Comparison of three back-propagation training algorithm for two case study, Indian J. Eng. Mater. Sci., vol. 12, no. October, pp. 434442, 2005.

[20] A. R. Webb, D. Lowe, and M. D. Bedworth, A comparison of non-linear optimisation strategies for feed-forward adaptive layered networks. RSRE Memorandum 4157, Royal Signals and Radar Establishment, St Andrew’s Road, Malvern, UK, 1988.

[21] EASA Airworthiness Directive AD No.: 2013-0068, 29 March 2013.

[22] EASA Airworthiness Directive AD No.: 2015-0135, 15 July 2015.

[23] T. Golly, D. M. Holm, ”Magnetic Angle of Attack Sensor”, WO 01/77622 A2, 2001.

[24] G. A. Seidel, D. J. Cronin, J. H. Mette, M. R. Koosmann, J. A. Schmitz, J. R. Fedele, D. A. Kromer, ”Multi-function Air Data Sensing Probe Having an Angle of Attack Vane”, US 6941805 B2, 2005.

[25] D. H. Lenschow, Vanes for Sensing Incidence Angles of the Air from an Aircraft, J. Appl. Meteorol., vol. 10, no. 6, pp. 13391343, Dec. 1971.

[26] UTC Aerospace Systems, Outside Air Temperature (OAT) Sensor Series 0129, Burnsville, USA.

[27] UTC Aerospace Systems, Angle of Attack (AOA) Sensors, Burnsville, USA.

[28] Aerosonic Corporation, Sensors, Clearwater, USA.

[29] AMETEK Aerospace, Angle of Attack Transducer, Wilmington, USA.

[30] AMETEK Aerospace, Aircraft Sensors and Systems Total Air Probe, Wilmington, USA.

[31] SpaceAge Control, State-of-the-Art Air Data Products Solution Guide, Palmdale, USA.

[32] W. Denson, G. Chandler, W. Crowell, A. Clark, & P. Jaworski, Nonelectronic Parts Reliability Data 1991 (No. RAC-NPRD-91). Reliability Analysis Center Griffiss AFB NY, 1991.

[33] S. Chiesa, S. C. Aleina, G. A. Di Meo, R. Fusaro, N. Viola Autonomous Take-off and Landing for Unmanned Aircraft System: Risk and Safety Analysis, 29th Congress of the International Council of the Aeronautical Sciences, ICAS 2014.

[34] F. De Vivo, M. Battipede, P. Gili, A. Brandl, ”Ill-conditioned problems improvement adapting Joseph covariance formula to non-linear Bayesian filters”. WSEAS Trans. Electron. 7, 1825, DOI:10.13140/RG.2.1.3027.0960, 2016.

[35] F. De Vivo, A. Brandl, M. Battipede, and P. Gili, Joseph covariance formula adaptation to Square-Root Sigma-Point Kalman filters, DOI: 10.1007/s11071-017-3356-x, Nonlinear Dynamics, Jan. 2017.

[36] G. Hardier, C. Seren, P. Ezerzere and G. Puyou, ”Aerodynamic Model Inversion for Virtual Sensing of Longitudinal Flight Parameters”, 2013 Conference on Control and Fault-Tolerant Systems (SysTol), pp. 140-145, Oct 2013.

[37] T. Rajkumar, J. Bardina, ”Prediction of Aerodynamic Coefficients using Neural Networks for Sparse Data”, FLAIRS Conference, pp. 242-246, 2002.

[38] T. Rajkumar, J. Bardina, ”Training data requirement for a neural network to predict aerodynamic coefficients”, Independent Component Analyses, Wavelets, and Neural Networks, vol. 5102, pp.92-103, Apr 2003.

[39] P. A. Samara, G. N. Fouskitakis, J. S. Sakellariou, & S. D. Fassois, ”Aircraft angle-of-attack virtual sensor design via a functional pooling NARX methodology”. European Control Conference (ECC), 2003, pp. 1816-1821, Sept 2003.

[40] D. Kriesel, A Brief Introduction to Neural Networks, http://www.dkriesel.com, 2007.