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Open AccessArticle

Adaptive Neural Motion Control of a Quadrotor UAV

1
División de Mecatrónica, Tecnológico de Estudios Superiores de Tianguistenco, Carretera Tenango—La Marquesa Km. 22, Santiago Tilapa, Tianguistenco, Mexico State C.P. 52650, Mexico
2
División de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad Nacional Autónoma de México, Av. Universidad 3000, Cd. Universitaria, Delegación Coyoacán, Mexico City C.P. 04510, Mexico
3
Departamento de Energía, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Av. San Pablo No. 180, Col. Reynosa Tamaulipas, Mexico City C.P. 02200, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 22 May 2020 / Revised: 12 July 2020 / Accepted: 16 July 2020 / Published: 20 July 2020
(This article belongs to the Special Issue Autonomous Vehicle Control)
Unmanned Aerial Vehicles have generated considerable interest in different research fields. The motion control problem is among the most important issues to be solved since system dynamic stability depends on the robustness of the main controller against endogenous and exogenous disturbances. In spite of different controllers have been introduced in the literature for motion control of fixed and rotary wing vehicles, there are some challenges for improving controller features such as simplicity, robustness, efficiency, adaptability, and stability. This paper outlines a novel approach to deal with the induced effects of external disturbances affecting the flight of a quadrotor unmanned aerial vehicle. The aim of our study is to further extend the current knowledge of quadrotor motion control by using both adaptive and robust control strategies. A new adaptive neural trajectory tracking control strategy based on B-spline artificial neural networks and on-line disturbance estimation for a quadrotor is proposed. A linear extended state observer is used for estimating time-varying disturbances affecting the controlled nonlinear system dynamics. B-spline artificial neural networks are properly synthesized for on-line calculating control gains of an adaptive Proportional Integral Derivative (PID) scheme. Simulation results highlight the implementation of such a controller is able to reject disturbances meanwhile perform proper motion control by exploiting the robustness, disturbance rejection, adaptability, and self-learning capabilities. View Full-Text
Keywords: quadrotor; motion control; disturbance rejection; artificial neural networks; B-spline quadrotor; motion control; disturbance rejection; artificial neural networks; B-spline
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MDPI and ACS Style

Yañez-Badillo, H.; Tapia-Olvera, R.; Beltran-Carbajal, F. Adaptive Neural Motion Control of a Quadrotor UAV. Vehicles 2020, 2, 468-490. https://0-doi-org.brum.beds.ac.uk/10.3390/vehicles2030026

AMA Style

Yañez-Badillo H, Tapia-Olvera R, Beltran-Carbajal F. Adaptive Neural Motion Control of a Quadrotor UAV. Vehicles. 2020; 2(3):468-490. https://0-doi-org.brum.beds.ac.uk/10.3390/vehicles2030026

Chicago/Turabian Style

Yañez-Badillo, Hugo; Tapia-Olvera, Ruben; Beltran-Carbajal, Francisco. 2020. "Adaptive Neural Motion Control of a Quadrotor UAV" Vehicles 2, no. 3: 468-490. https://0-doi-org.brum.beds.ac.uk/10.3390/vehicles2030026

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