<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Quadrotor unmanned aerial vehicles (UAVs) are small, agile four-rotor systems suitable for various applications, from surveillance to disaster support missions. Hence, achieving high-precision trajectory tracking is crucial for their successful deployment. This research focuses on modelling, parameter identification, and Linear Quadratic Regulator (LQR) control design for quadrotors, aiming to enhance their trajectory following capabilities. The quadrotor dynamics are a sixth degree-of-freedom (6DOF) equation of motion derived from Newton’s second law, encompassing moment of inertia, centre of gravity, weight, and thrust of propeller parameters. Experimental measurements are conducted to accurately determine these parameters, ensuring a realistic representation of the quadrotor system. Subsequently, a linearized model is constructed to provide a suitable plant for control system development. The LQR control design is intended to improve the trajectory tracking performance. This control strategy is validated through simulation and practical experiments, demonstrating its effectiveness in achieving high-precision trajectory following capabilites. The proposed approach demonstrates that LQR control effectively guides the quadrotor to resemble a predefined trajectory, experiencing only 3 % overshoot observed during the initial phase of flight.</p>
</abstract>

Quadrotor unmanned aerial vehicles (UAVs) are small, agile four-rotor systems suitable for various applications, from surveillance to disaster support missions. Hence, achieving high-precision trajectory tracking is crucial for their successful deployment. This research focuses on modelling, parameter identification, and Linear Quadratic Regulator (LQR) control design for quadrotors, aiming to enhance their trajectory following capabilities. The quadrotor dynamics are a sixth degree-of-freedom (6DOF) equation of motion derived from Newton’s second law, encompassing moment of inertia, centre of gravity, weight, and thrust of propeller parameters. Experimental measurements are conducted to accurately determine these parameters, ensuring a realistic representation of the quadrotor system. Subsequently, a linearized model is constructed to provide a suitable plant for control system development. The LQR control design is intended to improve the trajectory tracking performance. This control strategy is validated through simulation and practical experiments, demonstrating its effectiveness in achieving high-precision trajectory following capabilites. The proposed approach demonstrates that LQR control effectively guides the quadrotor to resemble a predefined trajectory, experiencing only 3 % overshoot observed during the initial phase of flight.

Towards High-Precision Quadrotor Trajectory Following Capabilities: Modelling, Parameter Estimation, and LQR Control

Latvian Journal of Physics and Technical Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Quadrotor unmanned aerial vehicles (UAVs) are small, agile four-rotor systems suitable for various applications, from surveillance to disaster support...

Towards High-Precision Quadrotor Trajectory Following Capabilities: Modelling, Parameter Estimation, and LQR Control

Published Online: Mar 30, 2024

Page range: 89 - 104

DOI: https://doi.org/10.2478/lpts-2024-0015

Keywords
Control, LQR, modelling, parameter estimation, quadrotor, unmanned aerial vehicles (UAVs)

© 2024 A. Hanif et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Towards High-Precision Quadrotor Trajectory Following Capabilities: Modelling, Parameter Estimation, and LQR Control

Published Online: Mar 30, 2024

Page range: 89 - 104

DOI: https://doi.org/10.2478/lpts-2024-0015

KeywordsControl, LQR, modelling, parameter estimation, quadrotor, unmanned aerial vehicles (UAVs)

© 2024 A. Hanif et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Control, LQR, modelling, parameter estimation, quadrotor, unmanned aerial vehicles (UAVs)