IEEE/CAA Journal of Automatica Sinica
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 multirotor 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 
[1] 
T. P. Nascimento and M. Saska, “Position and attitude control of multirotor 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 tensordecompositionbased globally optimal control and onboard visualinertial 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 frequencydomain flight dynamics modeling for multirotor 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, “Greybox 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: 10.2514/6.20173813

[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 dynamicallyfavourable quadrotor 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: 10.2514/6.20076461

[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. DeruazPepin, “Novel modelbased control mixing strategy for a coaxial pushpull 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,” IFACPapersOnLine, 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: 10.2514/6.20024667

[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 multirotor helicopter,” in ISPRSInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011, pp. 95−100. DOI: 10.5194/isprsarchivesXXXVIII1C22952011

[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, “Faulttolerant 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 neuralnetworkbased 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 nonsingular 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 neuroadaptive 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 faulttolerant 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 faulttolerant 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: 10.1016/08936080(89)900208

[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. GonzalezDiaz, E. Mainar, E. PaluzoHidalgo, and B. Rubio, “Neuralnetworkbased 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. ElMelegy, “Random sampler Mestimator algorithm with sequential probability ratio test for robust function approximation via feedforward neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 7, pp. 1074–1085, Jul. 2013. doi: 10.1109/TNNLS.2013.2251001
