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Modelling
Volume 6
Issue 4
10.3390/modelling6040157
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Article

Neural Network Approach for the Estimation of Quadrotor Aerodynamic and Inertial Parameters

by
Alejandro Jimenez-Flores
1,
Pablo A. Tellez-Belkotosky
1,
Edmundo Javier Ollervides-Vazquez
1,2,*,
Luis Arturo Reyes-Osorio
1,
Luis Amezquita-Brooks
1 and
Octavio Garcia-Salazar
1,*
1
Aerospace Engineering Research and Innovation Center, Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo Leon, Apodaca 66616, Mexico
2
Technological Institute of La Laguna-TecNM, Torreon 27000, Mexico
*
Authors to whom correspondence should be addressed.
Modelling 2025, 6(4), 157; https://doi.org/10.3390/modelling6040157 (registering DOI)
Submission received: 7 September 2025 / Revised: 12 November 2025 / Accepted: 25 November 2025 / Published: 30 November 2025
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Modelling)
Versions Notes

Abstract

The translational and rotational dynamics of quadrotor UAVs are commonly described by mathematical modeling where aerodynamic and inertial parameters are involved. Therefore, the importance of having accurate parameters in the model is critical for the correct performance of the UAV. In this paper, Artificial Neural Networks (ANNs) are used to estimate the aerodynamic and inertial parameters corresponding to the mathematical model of a quadrotor. Thrust and torque coefficients from the rotor models and the quadrotor inertia matrix are estimated by proposing and training two different ANN models implementing the back-propagation algorithm, using both experimental and simulation data. The estimated parameters are then compared with the reference parameters by means of quadrotor attitude simulations, showing high accuracy in their behavior. The results have shown that the proposed ANN models can accurately estimate both the aerodynamic and inertial parameters of a quadrotor UAV model using both experimental and simulation data, thus contributing to increasing the tools available for parameter estimation.
Keywords: neural network; quadrotor UAV; mathematical modeling; parameter estimation; aerodynamic coefficients; inertia matrix neural network; quadrotor UAV; mathematical modeling; parameter estimation; aerodynamic coefficients; inertia matrix

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MDPI and ACS Style

Jimenez-Flores, A.; Tellez-Belkotosky, P.A.; Ollervides-Vazquez, E.J.; Reyes-Osorio, L.A.; Amezquita-Brooks, L.; Garcia-Salazar, O. Neural Network Approach for the Estimation of Quadrotor Aerodynamic and Inertial Parameters. Modelling 2025, 6, 157. https://doi.org/10.3390/modelling6040157

AMA Style

Jimenez-Flores A, Tellez-Belkotosky PA, Ollervides-Vazquez EJ, Reyes-Osorio LA, Amezquita-Brooks L, Garcia-Salazar O. Neural Network Approach for the Estimation of Quadrotor Aerodynamic and Inertial Parameters. Modelling. 2025; 6(4):157. https://doi.org/10.3390/modelling6040157

Chicago/Turabian Style

Jimenez-Flores, Alejandro, Pablo A. Tellez-Belkotosky, Edmundo Javier Ollervides-Vazquez, Luis Arturo Reyes-Osorio, Luis Amezquita-Brooks, and Octavio Garcia-Salazar. 2025. "Neural Network Approach for the Estimation of Quadrotor Aerodynamic and Inertial Parameters" Modelling 6, no. 4: 157. https://doi.org/10.3390/modelling6040157

APA Style

Jimenez-Flores, A., Tellez-Belkotosky, P. A., Ollervides-Vazquez, E. J., Reyes-Osorio, L. A., Amezquita-Brooks, L., & Garcia-Salazar, O. (2025). Neural Network Approach for the Estimation of Quadrotor Aerodynamic and Inertial Parameters. Modelling, 6(4), 157. https://doi.org/10.3390/modelling6040157

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