Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

Sarah P. Madruga; Augusto H. B. M. Tavares; Saulo O. D. Luiz; Tiago P. do Nascimento; Antonio Marcus N. Lima

doi:10.1109/JAS.2021.1004266

Volume 9 Issue 2

Feb. 2022

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(2): 295-312

S. P. Madruga, A. H. B. M. Tavares, S. O. D. Luiz, T. P. Nascimento, and A. M. N. Lima, “Aerodynamic effects compensation on multi-rotor UAVs based on a neural network control allocation approach,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 295–312, Feb. 2022. doi: 10.1109/JAS.2021.1004266

Citation:

S. P. Madruga, A. H. B. M. Tavares, S. O. D. Luiz, T. P. Nascimento, and A. M. N. Lima, “Aerodynamic effects compensation on multi-rotor UAVs based on a neural network control allocation approach,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 295–312, Feb. 2022. doi: 10.1109/JAS.2021.1004266

Citation:

S. P. Madruga, A. H. B. M. Tavares, S. O. D. Luiz, T. P. Nascimento, and A. M. N. Lima, “Aerodynamic effects compensation on multi-rotor UAVs based on a neural network control allocation approach,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 295–312, Feb. 2022. doi: 10.1109/JAS.2021.1004266

PDF( 2732 KB)

Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

doi: 10.1109/JAS.2021.1004266

1.
Graduate Program of Electrical Engineering, Universidade Federal de Campina Grande, Campina Grande-PB, Brazil
2.
Department of Electrical Engineering, Universidade Federal de Campina Grande, Campina Grande-PB, Brazil
3.
Department of Computer Systems, Universidade Federal da Paraiba, Joao Pessoa-PB, Brazil

More Information

Author Bio:
Sarah P. Madruga received the bachelor degree in electrical engineering in 2016 from the Universidade Federal de Campina Grande, the master degree in computer sciences in 2018 from the Universidade Federal da Paraíba, and is currently pursuing the Ph.D. degree in electrical engineering at the Universidade Federal de Campina Grande. Her research is mainly focused on unmanned aerial vehicles, robotics and control systems

Augusto H. B. M. Tavares received the bachelor degree in electrical engineering in 2016 from the Universidade Federal de Campina Grande, the master degree in computer science in 2018 from the Universidade Federal da Paraíba, and is currently pursuing the Ph.D. degree in electrical engineering at the Universidade Federal de Campina Grande. His research is focused on robotics, unmanned aerial vehicles, battery modeling, control systems and model-based design

Saulo O. D. Luiz received the bachelor, master, and doctoral degrees in electrical engineering from the Universidade Federal de Campina Grande, Brazil, in 2006, 2008, and 2012, respectively. From July 2009 to November 2010, he was a Professor in the Colegiado de Engenharia da Computação at the Universidade Federal do Vale do São Francisco, Brazil. Since November 2010, he is a Professor in the Department of Electrical Engineering at the Universidade Federal de Campina Grande. From April 2017 to January 2018, he was a Visiting Researcher in the Technical University of Munich, Germany, where he developed activities on model-based design. His research interests include control systems, model-based design, battery modeling, and unmanned aerial vehicles

Tiago P. do Nascimento (Member, IEEE) received the B.S. degree in mechatronics engineering at the College of Technology and Science-UNIFTC (Brazil) in 2007, the M.S. degree in electrical engineering from Federal University of Bahia (Brazil) in 2009, and the Ph.D. degree from Oporto University (Portugal) in electrical and computer engineering, in 2012. He is currently an Assistant Professor at the Department Computer Systems, Universidade Federal da Paraiba (Brazil) since 2013, and also a Senior Researcher ate the Cybernetics Department, Czech Technical University in Prague (Czech Republic)

Antonio Marcus N. Lima (Senior Member, IEEE) received the bachelor and master degrees in electrical engineering from the Universidade Federal da Paraíba (UFPB), Campina Grande, Brazil, in 1982 and 1985, respectively, and the Ph.D. degree in electrical engineering from the Institut National Polytechnique de Toulouse, France, in 1989. He worked at the Escola Técnica Redentorista, Campina Grande, from 1977 to 1982, and was a Project Engineer at Sul-América Philips, Brazil, from 1982 to 1983. From March 1983 to March 2002, he worked at the Electrical Engineering Department of UFPB, where he became a Full Professor in 1996. At UFPB, he was a Coordinator of Graduate Studies from 1991 to 1993 and from 1997 to 2002. Since April 2002, he has been with the Department of Electrical Engineering, Universidade Federal de Campina Grande (UFCG), where he is a Full Professor. He was the Head of the Department of Electrical Engineering, UFCG, from 2002 to 2010. In 2010, he was admitted to the National Order of Scientific Merit in the category of Commander. His current research interests are in the fields of power electronics, automatic control, embedded systems, electronic instrumentation, biosensors, and biomedical engineering. In 2014, he was elected as a Full Member of the Brazilian Academy of Sciences and the National Academy of Engineering
Corresponding author: Tiago P. do Nascimento, e-mail: tiagopn@ci.ufpb.br
Received Date: 2021-06-01
Revised Date: 2021-08-04
Accepted Date: 2021-08-01

Available Online: 2021-08-27

Abstract

Abstract

This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks. Thus, the system performance can be improved by replacing the classic allocation matrix, without using the aerodynamic inflow equations directly. The network training is performed offline, which requires low computational power. The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm. Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work, because of its small size. We compared the mechanical torques commanded by the flight controller, i.e., the control input, to those actually generated by the actuators and established at the aircraft. It was observed that the proposed neural network was able to closely match them, while the classic allocation matrix could not achieve that. The allocation error was also determined in both cases. Furthermore, the closed-loop performance also improved with the use of the proposed neural network control allocation, as well as the quality of the thrust and torque signals, in which we perceived a much less noisy behavior.
- Aerodynamics effects,
- control allocation,
- minidrone,
- multi-rotor UAV,
- neural networks

FullText(HTML)

References(49)

References

[1]	T. P. Nascimento and M. Saska, “Position and attitude control of multi-rotor aerial vehicles: A survey,” Annu. Rev. Control, vol. 48, no. 1, pp. 29–146, Aug. 2019.
[2]	P. G. Fahlstrom and T. J. Gleason, Introduction to UAV Systems. 4th ed. Chichester, UK: John Wiley & Sons, 2012.
[3]	Y. Bouktir, M. Haddad, and T. Chettibi, “Trajectory planning for a quadrotor helicopter,” in Proc. 16th Mediterranean Conf. Control and Automation, Ajaccio, France, 2008, pp. 1258−1263.
[4]	M. T. Hussein and M. N. Nemah, “Modeling and control of quadrotor systems,” in Proc. 3rd RSI Int. Conf. Robotics and Mechatronics, Tehran, Iran, 2015, pp. 725−730.
[5]	T. A. Johansen and T. I. Fossen, “Control allocation–A survey,” Automatica, vol. 49, no. 5, pp. 1087–1103, May 2013. doi: 10.1016/j.automatica.2013.01.035
[6]	O. Härkegård, “Backstepping and control allocation with applications to flight control,” Ph.D. dissertation, Linköping University, Sweden, 2003.
[7]	R. Rashad, J. Goerres, R. Aarts, J. B. C. Engelen, and S. Stramigioli, “Fully actuated multirotor UAVs: A literature review,” IEEE Robot. Autom. Mag., vol. 27, no. 3, pp. 97–107, Sept. 2020. doi: 10.1109/MRA.2019.2955964
[8]	J. G. Alves, “Control allocation applied to robots subject to input constraints,” Ph.D. dissertation, Universidade Federal do Rio de Janeiro, Brazil, 2019.
[9]	H. M. Huang, G. M. Hoffmann, S. L. Waslander, and C. J. Tomlin, “Aerodynamics and control of autonomous quadrotor helicopters in aggressive maneuvering,” in Proc. IEEE Int. Conf. Robotics and Automation, Kobe, Japan, 2009, pp. 3277−3282.
[10]	F. Riether, “Agile quadrotor maneuvering using tensor-decomposition-based globally optimal control and onboard visual-inertial estimation,” Ph.D. dissertation, Massachusetts Institute of Technology, Cambridge, Massachusetts, 2016.
[11]	G. W. Cai, T. Taha, J. Dias, and L. Seneviratne, “A framework of frequency-domain flight dynamics modeling for multi-rotor aerial vehicles,” Proc. Inst. Mech. Eng. Part G:J. Aerosp. Eng., vol. 231, no. 1, pp. 30–46, Jan. 2017. doi: 10.1177/0954410016648348
[12]	P. Kantue and J. O. Pedro, “Grey-box modelling of an unmanned quadcopter during aggressive maneuvers,” in Proc. 22nd Int. Conf. System Theory, Control and Computing, Sinaia, Romania, 2018, pp. 640−645.
[13]	E. Davis and P. E. I. Pounds, “Direct sensing of thrust and velocity for a quadrotor rotor array,” IEEE Robot. Autom. Lett., vol. 2, no. 3, pp. 1360–1366, Jul. 2017. doi: 10.1109/LRA.2017.2668471
[14]	Z. Ning, R. W. Wlezien, and H. Hu, “An experimental study on small UAV propellers with serrated trailing edges,” in Proc. 47th AIAA Fluid Dynamics Conf., Denver, Colorado, 2017, pp. 1−17. DOI: 10.2514/6.2017-3813
[15]	C. Powers, D. Mellinger, A. Kushleyev, B. Kothmann, and V. Kumar, “Influence of aerodynamics and proximity effects in quadrotor flight,” in Experimental Robotics, J. Desai, G. Dudek, O. Khatib, and V. Kumar, Eds. Heidelberg: Springer, 2013, pp. 289−302.
[16]	P. Pounds, R. Mahony, J. Gresham, P. Corke, and J. Roberts, “Towards dynamically-favourable quad-rotor aerial robots,” in Proc. Australasian Conf. Robotics and Automation, N. Barnes and D. Austin, Eds. Australia: The Australian Robotics and Automation Association Inc., 2004, pp. 1−10.
[17]	P. Pounds, R. Mahony, and P. Corke, “Modelling and control of a large quadrotor robot,” Control Eng. Pract., vol. 18, no. 7, pp. 691–699, Jul. 2010. doi: 10.1016/j.conengprac.2010.02.008
[18]	P. J. Bristeau, P. Martin, E. Salaün, and N. Petit, “The role of propeller aerodynamics in the model of a quadrotor UAV,” in Proc. European Control Conf., Budapest, Hungary, 2009, pp. 683−688.
[19]	G. Hoffmann, H. M. Huang, S. Waslander, and C. Tomlin, “Quadrotor helicopter flight dynamics and control: Theory and experiment,” in Proc. AIAA Guidance, Navigation and Control Conf. Exhibit, Hilton Head, South Carolina, 2007, pp. 1−20. DOI: 10.2514/6.2007-6461
[20]	R. Niemiec and F. Gandhi, “Effects of inflow model on simulated aeromechanics of a quadrotor helicopter,” in Proc. AHS 72nd Annual Forum, West Palm Beach, Florida, 2016, pp. 1−13.
[21]	P. Kantue and J. O. Pedro, “Nonlinear identification of an unmanned quadcopter rotor dynamics using RBF neural networks,” in Proc. 22nd Int. Conf. System Theory, Control and Computing, Sinaia, Romania, 2018, pp. 292−298.
[22]	S. Omari, M. D. Hua, G. Ducard, and T. Hamel, “Nonlinear control of VTOL UAVs incorporating flapping dynamics,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Tokyo, Japan, 2013, pp. 2419−2425.
[23]	J. Chebbi, F. Defaÿ, Y. Brière, and A. Deruaz-Pepin, “Novel model-based control mixing strategy for a coaxial push-pull multirotor,” IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 485–491, Apr. 2020. doi: 10.1109/LRA.2019.2963652
[24]	M. Kirchengast, M. Steinberger, and M. Horn, “Control allocation under actuator saturation: An experimental evaluation,” IFAC-PapersOnLine, vol. 51, no. 25, pp. 48–54, Sept. 2018. doi: 10.1016/j.ifacol.2018.11.080
[25]	J. C. Monteiro, F. Lizarralde, and L. Hsu, “Optimal control allocation of quadrotor UAVs subject to actuator constraints,” in Proc. American Control Conf., Boston, MA, USA, 2016, pp. 500−505.
[26]	D. Doman and M. Oppenheimer, “Improving control allocation accuracy for nonlinear aircraft dynamics,” in Proc. AIAA Guidance, Navigation, and Control Conf. Exhibit, Monterey, California, 2002, pp. 1−10. DOI: 10.2514/6.2002-4667
[27]	E. Smeur, D. Höppener, and C. De Wagter, “Prioritized control allocation for quadrotors subject to saturation,” in Proc. Int. Micro Air Vehicle Conf. Flight Competition, Toulouse, France, 2017, pp. 37−43.
[28]	G. J. J. Ducard and M. D. Hua, “Discussion and practical aspects on control allocation for a multi-rotor helicopter,” in ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011, pp. 95−100. DOI: 10.5194/isprsarchives-XXXVIII-1-C22-95-2011
[29]	O. Härkegård, “Dynamic control allocation using constrained quadratic programming,” Journal of Guidance,Control,and Dynamics, vol. 27, no. 6, pp. 1028–1034, Nov.–Dec. 2004. doi: 10.2514/1.11607
[30]	T. Schneider, G. Ducard, R. Konrad, and S. Pascal, “Fault-tolerant control allocation for multirotor helicopters using parametric programming,” in Proc. Int. Micro Air Vehicle Conf. Flight Competition, Braunschweig, Germany, 2012.
[31]	Y. Wang, H. B. Zhang, and D. F. Han, “Neural network adaptive inverse model control method for quadrotor UAV,” in Proc. 35th Chinese Control Conf., Chengdu, China, 2016, pp. 3653−3658.
[32]	S. Cao, L. C. Shen, R. S. Zhang, H. C. Yu, and X. K. Wang, “Adaptive incremental nonlinear dynamic inversion control based on neural network for UAV maneuver,” in Proc. IEEE/ASME Int. Conf. Advanced Intelligent Mechatronics, Hong Kong, China, 2019, pp. 642−647.
[33]	F. Jiang, F. Pourpanah, and Q. Hao, “Design, implementation, and evaluation of a neural-network-based quadcopter UAV system,” IEEE Trans. Ind. Electron., vol. 67, no. 3, pp. 2076–2085, Mar. 2020. doi: 10.1109/TIE.2019.2905808
[34]	U. Ansari, A. Bajodah, and M. Hamayun, “Quadrotor control via robust generalized dynamic inversion and adaptive non-singular terminal sliding mode,” Asian J. Control, vol. 21, no. 3, pp. 1237–1249, May 2019. doi: 10.1002/asjc.1800
[35]	U. Ansari and A. H. Bajodah, “Quadrotor control using neuro-adaptive robust generalized dynamic inverasion,” in Proc. 8th Int. Conf. Systems and Control, Marrakesh, Morocco, 2019, pp. 1−6.
[36]	F. Achermann, N. R. J. Lawrance, R. Ranftl, A. Dosovitskiy, J. J. Chung, and R. Siegwart, “Learning to predict the wind for safe aerial vehicle planning,” in Proc. Int. Conf. Robotics and Automation, Montreal, QC, Canada, 2019, pp. 2311−2317.
[37]	G. Y. Shi, X. C. Shi, M. O’Connell, R. Yu, K. Azizzadenesheli, A. Anandkumar, Y. S. Yue, and S. J. Chung, “Neural lander: Stable drone landing control using learned dynamics,” in Proc. Int. Conf. Robotics and Automation, Montreal, QC, Canada, 2019, pp. 9784−9790.
[38]	J. Verberne and H. Moncayo, “Robust control architecture for wind rejection in quadrotors,” in Proc. Int. Conf. Unmanned Aircraft Systems, Atlanta, GA, USA, 2019, pp. 152−161.
[39]	A. Abbaspour, K. K. Yen, P. Forouzannezhad, and A. Sargolzaei, “A neural adaptive approach for active fault-tolerant control design in UAV,” IEEE Trans. Syst. Man Cybern.:Syst., vol. 50, no. 9, pp. 3401–3411, Sept. 2020. doi: 10.1109/TSMC.2018.2850701
[40]	Y. D. Song, L. He, D. Zhang, J. Y. Qian, and J. Fu, “Neuroadaptive fault-tolerant control of quadrotor UAVs: A more affordable solution,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 7, pp. 1975–1983, Jul. 2019. doi: 10.1109/TNNLS.2018.2876130
[41]	J. P. Reddinger and F. Gandhi, “Neural network and machine learning allocation of redundant controls for power optimization on a compound helicopter,” in Proc. AHS Int. 73rd Annu. Forum & Technology Display, Fort Worth, Texas, USA, 2017, pp. 1−13.
[42]	F. Sabatino, “Quadrotor control: Modeling, nonlinear control design, and simulation,” M.S. thesis, KTH, Stockholm, Sweden, 2015.
[43]	R. Mahony, V. Kumar, and P. Corke, “Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor,” IEEE Robot. Autom. Mag., vol. 19, no. 3, pp. 20–32, Sept. 2012. doi: 10.1109/MRA.2012.2206474
[44]	G. J. Leishman, Principles of Helicopter Aerodynamics with CD Extra. Cambridge: Cambridge University Press, 2006.
[45]	K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw., vol. 2, no. 5, pp. 359–366, Jul. 1989. doi: 10.1016/0893-6080(89)90020-8
[46]	A. N. Gorban and D. C. Wunsch, “The general approximation theorem,” in Proc. IEEE Int. Joint Conf. Neural Networks, Anchorage, AK, USA, 1998, pp. 1271−1274. DOI: 10.1109/IJCNN.1998.685957
[47]	R. Gonzalez-Diaz, E. Mainar, E. Paluzo-Hidalgo, and B. Rubio, “Neural-network-based curve fitting using totally positive rational bases,” Mathematics, vol. 8, no. 12, Dec. 2020. doi: 10.3390/math8122197
[48]	M. Li, S. Wibowo, and W. Guo, “Nonlinear curve fitting using extreme learning machines and radial basis function networks,” Comput. Sci. Eng., vol. 21, no. 5, pp. 6–15, Sept.–Oct. 2019. doi: 10.1109/MCSE.2018.2875323
[49]	M. T. El-Melegy, “Random sampler M-estimator algorithm with sequential probability ratio test for robust function approximation via feed-forward neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 7, pp. 1074–1085, Jul. 2013. doi: 10.1109/TNNLS.2013.2251001

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(22) / Tables(6)

Get Citation

PDF

XML

Article Metrics

Article views (3439) PDF downloads(118)

Highlights

Novel control allocation approach to take into account aerodynamic effects
Compensation of the allocation error caused by the classic matrix
Improved performance of the quadrotor controller and flight pergormance
Better overall signal quality

Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

doi: 10.1109/JAS.2021.1004266

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content