Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control

Njaratahiry Anicet Ranjalahy; Radoniaina Rasoamanana; Andry Auguste Randriamitantsoa

doi:doi:10.11648/j.acis.20261402.12

Research Article |

| Peer-Reviewed

Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control

Njaratahiry Anicet Ranjalahy^*

, Radoniaina Rasoamanana

, Andry Auguste Randriamitantsoa

Published in Automation, Control and Intelligent Systems (Volume 14, Issue 2)

Received: 20 August 2026 Accepted: 2 September 2026 Published: 20 September 2026

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Unmanned Aerial Vehicles (UAVs) have become increasingly important in surveillance, environmental monitoring, mapping, and scientific research, where flight stability and control performance are essential for safe and reliable operation. This study focuses on the modeling and stability analysis of an Aerosonde UAV using a Linear Quadratic Regulator (LQR) control approach. The main objective is to develop an optimal state-feedback controller capable of improving the stability and dynamic response of the UAV. A mathematical model of the Aerosonde UAV is established using longitudinal and lateral-directional flight dynamics and represented in state-space form. The LQR controller is designed by minimizing a quadratic performance index that balances state regulation and control effort through appropriate weighting matrices. The resulting feedback gains are applied to the longitudinal and lateral dynamic models, and numerical simulations are performed to compare the uncontrolled and LQR-controlled responses. The simulation results show that the LQR controller significantly improves the dynamic behavior of the UAV by reducing oscillations, decreasing overshoot, and accelerating convergence toward the equilibrium conditions. In the longitudinal dynamics, the controlled responses of velocity, vertical motion, pitch rate, altitude, and pitch angle exhibit improved stability compared with the uncontrolled system. Similarly, the lateral-directional responses demonstrate improved damping and faster convergence for lateral velocity, roll rate, yaw rate, roll angle, and yaw angle. These results confirm that the proposed LQR approach provides an effective state-feedback control strategy for improving the stability and dynamic performance of the Aerosonde UAV. The study provides a foundation for future investigations involving robust, adaptive, or intelligent control techniques under external disturbances and model uncertainties.

Published in	Automation, Control and Intelligent Systems (Volume 14, Issue 2)
DOI	10.11648/j.acis.20261402.12
Page(s)	37-49
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Unmanned Aerial Vehicle, Aerosonde UAV, Linear Quadratic Regulator, Flight Control, Stability Analysis, State Space Model

References

[1]	Beard, R. W., McLain, T. W. Small Unmanned Aircraft: Theory and Practice. Princeton, NJ: Princeton University Press; 2012. https://doi.org/10.1515/9781400840601
[2]	Stevens, B. L., Lewis, F. L., Johnson, E. N. Aircraft Control and Simulation: Dynamics, Controls Design, and Autonomous Systems. 3rd Edition. Hoboken, NJ: John Wiley & Sons; 2015. https://doi.org/10.1002/9781119174882
[3]	Anderson, B. D. O., Moore, J. B. Optimal Control: Linear Quadratic Methods. Englewood Cliffs, NJ: Prentice Hall; 1990.
[4]	Nelson, R. C. Flight Stability and Automatic Control. 2nd Edition. New York, NY: McGraw-Hill; 1998.
[5]	Cook, M. V. Flight Dynamics Principles. 3rd Edition. Oxford, UK: Butterworth-Heinemann; 2013. https://doi.org/10.1016/C2010-0-65889-5
[6]	Etkin, B., Reid, L. D. Dynamics of Flight: Stability and Control. 3rd Edition. New York, NY: John Wiley & Sons; 1996.
[7]	Franklin, G. F., Powell, J. D., Emami-Naeini, A. Feedback Control of Dynamic Systems. 7th Edition. Boston, MA: Pearson; 2015.
[8]	Skogestad, S., Postlethwaite, I. Multivariable Feedback Control: Analysis and Design. 2nd Edition. Chichester, UK: John Wiley & Sons; 2005.
[9]	Admas, Y. A., Mitiku, H. M., Salau, A. O., Omeje, C. O., & Braide, S. L. (2024). Control of a fixed wing unmanned aerial vehicle using a higher-order sliding mode controller and non-linear PID controller. Scientific Reports, 14, 23139. https://doi.org/10.1038/s41598-024-73901-y
[10]	Wang, B., Yan, Y., Xiong, X., Han, Q., & Li, Z. (2024). Attitude control of small fixed-wing UAV based on sliding mode and linear active disturbance rejection control. Drones, 8(7), 318. https://doi.org/10.3390/drones8070318
[11]	Ulus, Ş., & Eski, İ. (2026). A comprehensive assessment of control strategies for longitudinal and lateral flight of fixed-wing UAVs. The Aeronautical Journal, 130(1349), 2207–2237. https://doi.org/10.1017/aer.2026.10152
[12]	Salazar, E., Lozano, R., & Salazar, S. (2025). Nonlinear feedback linearization control and region of attraction analysis for a fixed-wing UAV. Drones, 9(9), 606. https://doi.org/10.3390/drones9090606
[13]	Metekia, E. W., Asfaw, W. A., Abdissa, C. M., & Lemma, L. N. (2025). Control of a fixed wing unmanned aerial vehicle using a robust fractional order controller. Scientific Reports, 15, 19954. https://doi.org/10.1038/s41598-025-03552-0
[14]	Wu, E., Wang, P., & Guo, Z. (2025). Adaptive robust optimal control for UAV taxiing systems with uncertainties. Drones, 9(10), 668. https://doi.org/10.3390/drones9100668
[15]	Zhou, Z., Liu, F., & Chen, M. (2026). Dynamic output feedback L₂–L∞ resilient control of uncertain fixed-wing UAV under composite disturbances. Journal of Control and Decision, 13(1), 148–158. https://doi.org/10.1080/23307706.2025.2576123

Cite This Article

Plain Text BibTeX RIS

APA Style

Ranjalahy, N. A., Rasoamanana, R., Randriamitantsoa, A. A. (2026). Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control. Automation, Control and Intelligent Systems, 14(2), 37-49. https://doi.org/10.11648/j.acis.20261402.12

Copy | Download

ACS Style

Ranjalahy, N. A.; Rasoamanana, R.; Randriamitantsoa, A. A. Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control. Autom. Control Intell. Syst. 2026, 14(2), 37-49. doi: 10.11648/j.acis.20261402.12

Copy | Download

AMA Style

Ranjalahy NA, Rasoamanana R, Randriamitantsoa AA. Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control. Autom Control Intell Syst. 2026;14(2):37-49. doi: 10.11648/j.acis.20261402.12

Copy | Download

@article{10.11648/j.acis.20261402.12,
  author = {Njaratahiry Anicet Ranjalahy and Radoniaina Rasoamanana and Andry Auguste Randriamitantsoa},
  title = {Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control},
  journal = {Automation, Control and Intelligent Systems},
  volume = {14},
  number = {2},
  pages = {37-49},
  doi = {10.11648/j.acis.20261402.12},
  url = {https://doi.org/10.11648/j.acis.20261402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20261402.12},
  abstract = {Unmanned Aerial Vehicles (UAVs) have become increasingly important in surveillance, environmental monitoring, mapping, and scientific research, where flight stability and control performance are essential for safe and reliable operation. This study focuses on the modeling and stability analysis of an Aerosonde UAV using a Linear Quadratic Regulator (LQR) control approach. The main objective is to develop an optimal state-feedback controller capable of improving the stability and dynamic response of the UAV. A mathematical model of the Aerosonde UAV is established using longitudinal and lateral-directional flight dynamics and represented in state-space form. The LQR controller is designed by minimizing a quadratic performance index that balances state regulation and control effort through appropriate weighting matrices. The resulting feedback gains are applied to the longitudinal and lateral dynamic models, and numerical simulations are performed to compare the uncontrolled and LQR-controlled responses. The simulation results show that the LQR controller significantly improves the dynamic behavior of the UAV by reducing oscillations, decreasing overshoot, and accelerating convergence toward the equilibrium conditions. In the longitudinal dynamics, the controlled responses of velocity, vertical motion, pitch rate, altitude, and pitch angle exhibit improved stability compared with the uncontrolled system. Similarly, the lateral-directional responses demonstrate improved damping and faster convergence for lateral velocity, roll rate, yaw rate, roll angle, and yaw angle. These results confirm that the proposed LQR approach provides an effective state-feedback control strategy for improving the stability and dynamic performance of the Aerosonde UAV. The study provides a foundation for future investigations involving robust, adaptive, or intelligent control techniques under external disturbances and model uncertainties.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control
AU  - Njaratahiry Anicet Ranjalahy
AU  - Radoniaina Rasoamanana
AU  - Andry Auguste Randriamitantsoa
Y1  - 2026/09/20
PY  - 2026
N1  - https://doi.org/10.11648/j.acis.20261402.12
DO  - 10.11648/j.acis.20261402.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 37
EP  - 49
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20261402.12
AB  - Unmanned Aerial Vehicles (UAVs) have become increasingly important in surveillance, environmental monitoring, mapping, and scientific research, where flight stability and control performance are essential for safe and reliable operation. This study focuses on the modeling and stability analysis of an Aerosonde UAV using a Linear Quadratic Regulator (LQR) control approach. The main objective is to develop an optimal state-feedback controller capable of improving the stability and dynamic response of the UAV. A mathematical model of the Aerosonde UAV is established using longitudinal and lateral-directional flight dynamics and represented in state-space form. The LQR controller is designed by minimizing a quadratic performance index that balances state regulation and control effort through appropriate weighting matrices. The resulting feedback gains are applied to the longitudinal and lateral dynamic models, and numerical simulations are performed to compare the uncontrolled and LQR-controlled responses. The simulation results show that the LQR controller significantly improves the dynamic behavior of the UAV by reducing oscillations, decreasing overshoot, and accelerating convergence toward the equilibrium conditions. In the longitudinal dynamics, the controlled responses of velocity, vertical motion, pitch rate, altitude, and pitch angle exhibit improved stability compared with the uncontrolled system. Similarly, the lateral-directional responses demonstrate improved damping and faster convergence for lateral velocity, roll rate, yaw rate, roll angle, and yaw angle. These results confirm that the proposed LQR approach provides an effective state-feedback control strategy for improving the stability and dynamic performance of the Aerosonde UAV. The study provides a foundation for future investigations involving robust, adaptive, or intelligent control techniques under external disturbances and model uncertainties.
VL  - 14
IS  - 2
ER  -

Copy | Download

Author Information

Njaratahiry Anicet Ranjalahy

Doctoral School of Engineering and Innovation Sciences and Technologies (ED-STII), University of Antananarivo, Antananarivo, Madagascar

Contact Email

http://orcid.org/0009-0001-7258-0628
Radoniaina Rasoamanana

Doctoral School of Engineering and Innovation Sciences and Technologies (ED-STII), University of Antananarivo, Antananarivo, Madagascar

Contact Email

http://orcid.org/0009-0009-4060-6246
Andry Auguste Randriamitantsoa

Doctoral School of Engineering and Innovation Sciences and Technologies (ED-STII), University of Antananarivo, Antananarivo, Madagascar

Contact Email

http://orcid.org/0009-0009-1755-8864

Download PDF

Submit an Article

Sections

Plain Text BibTeX RIS

APA Style

Ranjalahy, N. A., Rasoamanana, R., Randriamitantsoa, A. A. (2026). Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control. Automation, Control and Intelligent Systems, 14(2), 37-49. https://doi.org/10.11648/j.acis.20261402.12

Copy | Download

ACS Style

Ranjalahy, N. A.; Rasoamanana, R.; Randriamitantsoa, A. A. Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control. Autom. Control Intell. Syst. 2026, 14(2), 37-49. doi: 10.11648/j.acis.20261402.12

Copy | Download

AMA Style

Ranjalahy NA, Rasoamanana R, Randriamitantsoa AA. Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control. Autom Control Intell Syst. 2026;14(2):37-49. doi: 10.11648/j.acis.20261402.12

Copy | Download

@article{10.11648/j.acis.20261402.12,
  author = {Njaratahiry Anicet Ranjalahy and Radoniaina Rasoamanana and Andry Auguste Randriamitantsoa},
  title = {Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control},
  journal = {Automation, Control and Intelligent Systems},
  volume = {14},
  number = {2},
  pages = {37-49},
  doi = {10.11648/j.acis.20261402.12},
  url = {https://doi.org/10.11648/j.acis.20261402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20261402.12},
  abstract = {Unmanned Aerial Vehicles (UAVs) have become increasingly important in surveillance, environmental monitoring, mapping, and scientific research, where flight stability and control performance are essential for safe and reliable operation. This study focuses on the modeling and stability analysis of an Aerosonde UAV using a Linear Quadratic Regulator (LQR) control approach. The main objective is to develop an optimal state-feedback controller capable of improving the stability and dynamic response of the UAV. A mathematical model of the Aerosonde UAV is established using longitudinal and lateral-directional flight dynamics and represented in state-space form. The LQR controller is designed by minimizing a quadratic performance index that balances state regulation and control effort through appropriate weighting matrices. The resulting feedback gains are applied to the longitudinal and lateral dynamic models, and numerical simulations are performed to compare the uncontrolled and LQR-controlled responses. The simulation results show that the LQR controller significantly improves the dynamic behavior of the UAV by reducing oscillations, decreasing overshoot, and accelerating convergence toward the equilibrium conditions. In the longitudinal dynamics, the controlled responses of velocity, vertical motion, pitch rate, altitude, and pitch angle exhibit improved stability compared with the uncontrolled system. Similarly, the lateral-directional responses demonstrate improved damping and faster convergence for lateral velocity, roll rate, yaw rate, roll angle, and yaw angle. These results confirm that the proposed LQR approach provides an effective state-feedback control strategy for improving the stability and dynamic performance of the Aerosonde UAV. The study provides a foundation for future investigations involving robust, adaptive, or intelligent control techniques under external disturbances and model uncertainties.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Design and Stability Analysis of an Aerosonde UAV Using Linear Quadratic Regulator Control
AU  - Njaratahiry Anicet Ranjalahy
AU  - Radoniaina Rasoamanana
AU  - Andry Auguste Randriamitantsoa
Y1  - 2026/09/20
PY  - 2026
N1  - https://doi.org/10.11648/j.acis.20261402.12
DO  - 10.11648/j.acis.20261402.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 37
EP  - 49
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20261402.12
AB  - Unmanned Aerial Vehicles (UAVs) have become increasingly important in surveillance, environmental monitoring, mapping, and scientific research, where flight stability and control performance are essential for safe and reliable operation. This study focuses on the modeling and stability analysis of an Aerosonde UAV using a Linear Quadratic Regulator (LQR) control approach. The main objective is to develop an optimal state-feedback controller capable of improving the stability and dynamic response of the UAV. A mathematical model of the Aerosonde UAV is established using longitudinal and lateral-directional flight dynamics and represented in state-space form. The LQR controller is designed by minimizing a quadratic performance index that balances state regulation and control effort through appropriate weighting matrices. The resulting feedback gains are applied to the longitudinal and lateral dynamic models, and numerical simulations are performed to compare the uncontrolled and LQR-controlled responses. The simulation results show that the LQR controller significantly improves the dynamic behavior of the UAV by reducing oscillations, decreasing overshoot, and accelerating convergence toward the equilibrium conditions. In the longitudinal dynamics, the controlled responses of velocity, vertical motion, pitch rate, altitude, and pitch angle exhibit improved stability compared with the uncontrolled system. Similarly, the lateral-directional responses demonstrate improved damping and faster convergence for lateral velocity, roll rate, yaw rate, roll angle, and yaw angle. These results confirm that the proposed LQR approach provides an effective state-feedback control strategy for improving the stability and dynamic performance of the Aerosonde UAV. The study provides a foundation for future investigations involving robust, adaptive, or intelligent control techniques under external disturbances and model uncertainties.
VL  - 14
IS  - 2
ER  -

Copy | Download