Performance evaluation of developed controllers for unmanned aerial vehicles with reference camera

This research article aims to compare the performance of three distinct types of controllers, namely proportional-integral-derivative (PID), Fuzzy PID (F-PID), and Fuzzy PID controller with prefilter (PF-PID), for the “Quanser” QDrone. In quadrotor applications, control design and implementation are of significant significance. There have been several control methods developed for quadrotor control issues, including LQR, backstepping nonlinear control, and sliding mode control. In practice, PD cascaded feedback control is the most often utilized quadrotor control system, offering equivalent or even superior performance to more sophisticated controllers. The primary objective is to determine the most effective controller that ensures optimal stability and accuracy in controlling the drone’s movement. The novelty of this system lies in its utilization of six cameras to generate a highly accurate reference point for the drone. A MATLAB Simulink algorithm is developed to govern the quadrotor based on its dynamic model. The position of the unmanned aerial vehicle (UAV) is tracked using the Neutral point OptiTrack system, and the data gathered from this system is used as feedback for the control algorithm. The design and tuning techniques for the fuzzy logic controller that are used to achieve the most successful outcomes are presented in this article. The experiments are conducted with and without disturbances, and the results show that the fuzzy controller provides significantly stable flight in comparison with the PID controller alone. Additionally, experiments with a prefilter demonstrate that a prefilter contributes to an improvement in the PF-PID results during the tests without disturbance. Additional experiments are conducted to examine the accuracy of the UAV’s position change over a specified distance using different controllers. The outcomes of these tests are examined for consistency with the desired result, and it is found that the F-PID exhibited the highest level of accuracy in the positional control tests, showcasing its superior performance in achieving precise positioning of the UAV.

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Publication Details

Journal
Measurement and Control
Published
2024-11-20
DOI
https://doi.org/10.1177/00202940241270863
Citations
3
Primary Topic
Adaptive Control of Nonlinear Systems
Type
article
Field-Weighted Citation Impact
0.98

Funders

Controls
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article

Performance evaluation of developed controllers for unmanned aerial vehicles with reference camera

Ardak Kashkynbayev, Essam Shehab, Md. Hazrat Ali, Aidana Tassanbi et al.
3 citations
Measurement and Control
Adaptive Control of Nonlinear Systems
0.98
article

Performance evaluation of developed controllers for unmanned aerial vehicles with reference camera

Ardak Kashkynbayev, Essam Shehab, Md. Hazrat Ali, Aidana Tassanbi, Ton Duc Do, Anish Khan
article en
3 citations

Abstract

This research article aims to compare the performance of three distinct types of controllers, namely proportional-integral-derivative (PID), Fuzzy PID (F-PID), and Fuzzy PID controller with prefilter (PF-PID), for the “Quanser” QDrone. In quadrotor applications, control design and implementation are of significant significance. There have been several control methods developed for quadrotor control issues, including LQR, backstepping nonlinear control, and sliding mode control. In practice, PD cascaded feedback control is the most often utilized quadrotor control system, offering equivalent or even superior performance to more sophisticated controllers. The primary objective is to determine the most effective controller that ensures optimal stability and accuracy in controlling the drone’s movement. The novelty of this system lies in its utilization of six cameras to generate a highly accurate reference point for the drone. A MATLAB Simulink algorithm is developed to govern the quadrotor based on its dynamic model. The position of the unmanned aerial vehicle (UAV) is tracked using the Neutral point OptiTrack system, and the data gathered from this system is used as feedback for the control algorithm. The design and tuning techniques for the fuzzy logic controller that are used to achieve the most successful outcomes are presented in this article. The experiments are conducted with and without disturbances, and the results show that the fuzzy controller provides significantly stable flight in comparison with the PID controller alone. Additionally, experiments with a prefilter demonstrate that a prefilter contributes to an improvement in the PF-PID results during the tests without disturbance. Additional experiments are conducted to examine the accuracy of the UAV’s position change over a specified distance using different controllers. The outcomes of these tests are examined for consistency with the desired result, and it is found that the F-PID exhibited the highest level of accuracy in the positional control tests, showcasing its superior performance in achieving precise positioning of the UAV.

Measurement and ControlVol. 58(9)
Nazarbayev University (KZ)
Nazarbayev University
Openalex Percentile: Top 23%
Adaptive Control of Nonlinear Systems
0.98
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