Advantages of Neural Network Based Air Data Estimation for Unmanned Aerial Vehicles
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.
[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.
[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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:75598", author = "Angelo Lerro and Manuela Battipede and Piero Gili and Alberto Brandl", title = "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.", keywords = "Neural network, aerodynamic angles, virtual sensor,
unmanned aerial vehicle, air data system, flight test.", volume = "11", number = "5", pages = "1085-10", }
