Real-Time Quadcopter Path Tracking Using Fuzzy-PID Hybrid Controller

Jebri1, Manel; Alshareefi, Hamid; Hussein, Ahmed; Yousir, Naeem; Al-Tameemi, Rasha

doi:10.25673/123746

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 1411–1418

Real-Time Quadcopter Path Tracking Using Fuzzy-PID Hybrid Controller

Manel Jebri1, Hamid Alshareefi, Ahmed Abdullah Hussein, Naeem Th. Yousir, Rasha Abed Hussein and Ahmed Read Al-Tameemi

📄 Download PDF DOI: 10.25673/123746

Abstract

Quadcopter unmanned aerial vehicles (UAVs) are being used more and more for surveillance, disaster response, logistics, and smart infrastructure because they are easy to move and cheap. Nonetheless, attaining dependable real-time path tracking amidst nonlinear dynamics and external perturbations continues to be a significant challenge. This paper puts forward a hybrid Fuzzy-PID controller that combines fuzzy logic-based adaptive gain tuning with traditional PID control to enhance trajectory tracking performance. The methodology encompasses the modeling of the six-degree-of-freedom quadcopter dynamics, the design of a fuzzy inference system for adaptive gain adjustment, and its integration with PID within a closed-loop control framework. We ran simulation tests in MATLAB/Simulink for several paths (straight line, circle, and square) while there were wind disturbances and sensor noise. The results show that the Fuzzy-PID controller works much better than both classical PID and fuzzy-only controllers. It has a lower RMSE, converges faster, has less overshoot, and smoother control input signals. The suggested method is very strong and adaptable, which makes it a good choice for real-time UAV operations.

Keywords

Quadcopter UAV Fuzzy-PID Hybrid Controller Real-Time Path Tracking Adaptive Control Robustness Trajectory Tracking.

References

I. Lopez-Sanchez and J. Moreno-Valenzuela, “PID control of quadrotor UAVs: A survey,” Annual Reviews in Control, vol. 56, Art. no. 100900, 2023.
J. Dong and B. He, “Novel fuzzy PID-type iterative learning control for quadrotor UAV,” Sensors, vol. 19, no. 1, Art. no. 24, 2018.
E. A. Gedefaw, C. M. Abdissa, and L. N. Lemma, “An improved trajectory tracking control of quadcopter using a novel Sliding Mode Control with Fuzzy PID Surface,” PloS One, vol. 19, no. 11, Art. no. e0308997, 2024.
N. W. Madebo, “Hybrid adaptive PID control strategy for UAVs using combined neural networks and fuzzy logic,” PLoS One, vol. 20, no. 8, Art. no. e0331036, 2025.
T. Ran, L. Yuan, and J. B. Zhang, “Scene perception based visual navigation of mobile robot in indoor environment,” ISA Transactions, vol. 109, pp. 389-400, 2021.
R. Sharma, P. Gupta, and A. Singh, “Human-computer interaction frameworks for secure digital adoption,” International Journal of Human-Computer Interaction, vol. 41, no. 7, pp. 845-862, 2025, [Online]. Available: https://doi.org/10.1080/10447318.2025.2495843.
S. Kumar and R. Patel, “Blockchain-driven frameworks for secure healthcare data management,” in Proc. IEEE Int. Conf. Cloud Computing, pp. 1-8, 2025, [Online]. Available: https://doi.org/10.1109/11015778.
H. Ufacık and O. F. Kececioglu, “Interval type-2 fuzzy PID cascade control of quadrotor based on non-dominated sorting genetic algorithm-II,” The Aeronautical Journal, pp. 1-36, 2025.
O. Rodríguez-Abreo, J. Rodríguez-Reséndiz,
A. García-Cerezo, and J. R. García-Martínez, “Fuzzy logic controller for UAV with gains optimized via genetic algorithm,” Heliyon, vol. 10, no. 4, 2024.
A. Boubakir, T. Souanef, S. Labiod, and
J. F. Whidborne, “A robust adaptive PID-like controller for quadrotor unmanned aerial vehicle systems,” Aerospace, vol. 11, no. 12, Art. no. 980, 2024.
A. K. Al-Jiboory, “Adaptive quadrotor control using online dynamic mode decomposition,” European Journal of Control, vol. 80, Art. no. 101117, 2024.
Z. Wang, Q. Yuan, Y. Zhu, Y. Hu, H. Chen, X. Xie, and W. Gu, “Fast and Intelligent Proportional-Integral-Derivative (PID) Attitude Control of Quadrotor and Dual-Rotor Coaxial Unmanned Aerial Vehicles (UAVs) Based on All-True Composite Motion,” Drones, vol. 8, no. 12, Art. no. 747, 2024.
L. Xu, K. Qin, F. Tang, M. Shi, and B. Lin, “A novel attitude control strategy for a quadrotor drone with actuator dynamics based on a high-order sliding mode disturbance observer,” Drones, vol. 8, no. 4, Art. no. 131, 2024.
C. Qi, J. Li, and H. Li, “An attention transfer entropy based causality analysis with applications in rooting short-term disturbances for chemical processes,” ISA Transactions, vol. 136, pp. 284-296, 2023.
V. Mehta and S. Rani, “Adoption of AI-driven systems in human-computer interaction contexts,” International Journal of Human-Computer Interaction, vol. 41, no. 6, pp. 701-718, 2025, [Online]. Available: https://doi.org/10.1080/10447318.2025.2480826.
A. Bida and H. A. Naser, “Diagnostic of Osteoporosis Using Backpropagation Neural Networks,” Journal of Techniques, vol. 7, no. 2, pp. 10-20, 2025, [Online]. Available: https://doi.org/10.51173/jt.v7i2.2597.
M. T. Sadeghi and H. Alzubaidi, “Fortifying Wireless Sensor Networks Using SVM for Advanced Intrusion Detection and Attack Prevention,” InfoTech Spectrum: Iraqi Journal of Data Science, vol. 2, no. 2, pp. 1-13, 2025, [Online]. Available: https://doi.org/10.51173/ijds.v2i2.24.