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Advantages of Neural Network Based Air Data Estimation for Unmanned Aerial Vehicles
Abstract:Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this system in UAV applications. A simplification of the entire ADAHRS architecture will bring to reduce the overall cost together with improved safety performance. Smart-ADAHRS has currently reached Technology Readiness Level (TRL) 6. Real flight tests took place on ultralight aircraft equipped with a suitable Flight Test Instrumentation (FTI). The output of the algorithm using the flight test measurements demonstrates the capability for this fusion algorithm to embed in a single device multiple physical and virtual sensors. Any source of dynamic and static pressure can be integrated with this system gaining a significant improvement in terms of versatility.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130557Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 637
 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.
 K. C. Wong, Aerospace industry opportunities in Australia-unmanned aerial vehicles (UAVs). Department of Aeronautical Engineering, University of Sydney, 2007.
 FAA Modernization and Reform Act (2012). H.R. 658, 112th Congress, 2nd Session.
 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.
 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.
 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.
 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.
 A. Lerro, M. Battipede, P. Gili, ”Sistema e procedimento di misura e valutazione di dati aria e inerziali”, TO2013A000601, 2013.
 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.
 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.
 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.
 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.
 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.
 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.
 C. M. Bishop, Neural networks for pattern recognition, J. Am. Stat. Assoc., vol. 92, 1995.
 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.
 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.
 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.
 . 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.
 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.
 EASA Airworthiness Directive AD No.: 2013-0068, 29 March 2013.
 EASA Airworthiness Directive AD No.: 2015-0135, 15 July 2015.
 T. Golly, D. M. Holm, ”Magnetic Angle of Attack Sensor”, WO 01/77622 A2, 2001.
 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.
 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.
 UTC Aerospace Systems, Outside Air Temperature (OAT) Sensor Series 0129, Burnsville, USA.
 UTC Aerospace Systems, Angle of Attack (AOA) Sensors, Burnsville, USA.
 Aerosonic Corporation, Sensors, Clearwater, USA.
 AMETEK Aerospace, Angle of Attack Transducer, Wilmington, USA.
 AMETEK Aerospace, Aircraft Sensors and Systems Total Air Probe, Wilmington, USA.
 SpaceAge Control, State-of-the-Art Air Data Products Solution Guide, Palmdale, USA.
 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.
 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.
 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.
 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.
 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.
 T. Rajkumar, J. Bardina, ”Prediction of Aerodynamic Coefficients using Neural Networks for Sparse Data”, FLAIRS Conference, pp. 242-246, 2002.
 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.
 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.
 D. Kriesel, A Brief Introduction to Neural Networks, http://www.dkriesel.com, 2007.