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

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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

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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

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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

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References(49)

References

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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

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通讯作者: 陈斌, bchen63@163.com

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