Robust LQR and LQR-PI control strategies based on adaptive weighting matrix selection for a UAV position and attitude tracking control
Unmanned aerial vehicles (UAVs) are subject to the wind and other environmental disturbances that can destabilize them. The high performance and robustness of the Linear Quadratic Regulator (LQR) controllers ensure the ability to reduce the deviations in state trajectories with minimum control effor...
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Published in | Alexandria engineering journal Vol. 61; no. 8; pp. 6275 - 6292 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.08.2022
Elsevier |
Subjects | |
Online Access | Get full text |
ISSN | 1110-0168 |
DOI | 10.1016/j.aej.2021.11.057 |
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Abstract | Unmanned aerial vehicles (UAVs) are subject to the wind and other environmental disturbances that can destabilize them. The high performance and robustness of the Linear Quadratic Regulator (LQR) controllers ensure the ability to reduce the deviations in state trajectories with minimum control effort. In this paper, the weighting matrices are adjusted automatically via a novel method implemented through state variables matrix of the quadrotor together with a preference factor. This proposed factor is determined by the designer via penalizing or rewarding a certain state of the quadrotor out of others so that the model insufficiencies against disturbances are compensated. The effectiveness of the proposed method of selecting these matrices is evaluated for the cases of LQR and LQR with a PI controller. They were evaluated with respect to the convergence of state variables to their reference values within a specific time and compliance with their desired performance characteristics. The experimental results revealed that both control strategies could stabilize the quadrotor in the desired position and attitude with better performance and robustness characteristics. The results also showed that the LQR-PI control method is a more reliable and effective approach in achieving higher tracking performances compared to the LQR only. |
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AbstractList | Unmanned aerial vehicles (UAVs) are subject to the wind and other environmental disturbances that can destabilize them. The high performance and robustness of the Linear Quadratic Regulator (LQR) controllers ensure the ability to reduce the deviations in state trajectories with minimum control effort. In this paper, the weighting matrices are adjusted automatically via a novel method implemented through state variables matrix of the quadrotor together with a preference factor. This proposed factor is determined by the designer via penalizing or rewarding a certain state of the quadrotor out of others so that the model insufficiencies against disturbances are compensated. The effectiveness of the proposed method of selecting these matrices is evaluated for the cases of LQR and LQR with a PI controller. They were evaluated with respect to the convergence of state variables to their reference values within a specific time and compliance with their desired performance characteristics. The experimental results revealed that both control strategies could stabilize the quadrotor in the desired position and attitude with better performance and robustness characteristics. The results also showed that the LQR-PI control method is a more reliable and effective approach in achieving higher tracking performances compared to the LQR only. |
Author | Sir Elkhatem, Aisha Naci Engin, Seref |
Author_xml | – sequence: 1 givenname: Aisha surname: Sir Elkhatem fullname: Sir Elkhatem, Aisha email: f0415042@std.yildiz.edu.tr organization: Yildiz Technical University, Faculty of Electrical & Electronics, Control and Automation Eng. Dept, Davutpasa Campus, Esenler, Istanbul 34220, Turkey – sequence: 2 givenname: Seref surname: Naci Engin fullname: Naci Engin, Seref email: nengin@yildiz.edu.tr organization: Yildiz Technical University, Faculty of Electrical & Electronics, Control and Automation Eng. Dept, Davutpasa Campus, Esenler, Istanbul 34220, Turkey |
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Keywords | LQR-PI UAV LQR Mass uncertainty Actuator fault Weighting matrices |
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