A Review on the Application of PD and PID Controllers in UAV Attitude and Altitude Control
DOI:
https://doi.org/10.54097/g771pv31Keywords:
UAV control, PID controller, PD controller, Intelligent and adaptive control.Abstract
Unmanned Aerial Vehicles (UAVs) have become an essential component in civilian, industrial, and defense applications due to their high mobility, low operational cost, and ability to perform missions in dangerous environments. However, maintaining stability and accuracy during flight remains a critical challenge because UAVs are nonlinear, underactuated, and highly sensitive to environmental disturbances. Among the wide range of control strategies explored for UAVs, Proportional–Derivative (PD) and Proportional–Integral–Derivative (PID) controllers have retained their popularity because of their simplicity, reliability, and ease of implementation. This paper reviews the application of PD and PID controllers in UAV attitude and altitude control. It compares their fundamental design principles, implementation structures, performance characteristics, and recent advancements, including adaptive, fuzzy, and neural extensions. Through analysis of more than fifteen recent studies, the paper highlights that PD control provides fast transient performance suitable for agile maneuvers, whereas PID control ensures accurate steady-state tracking under persistent disturbances. The review concludes with a discussion of limitations in classical PID tuning and outlines future trends toward intelligent, hybrid, and AI-assisted PID architectures designed for next-generation UAV autonomy.
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[1] I. Sadeghzadeh, et al., “PD and PID-based attitude control of quadrotor UAVs,” IEEE Trans. Aerosp. Electron. Syst., 2019.
[2] L. Zhou and H. Zhang, “Ziegler–Nichols tuning of PID controller for UAV stabilization,” Control Eng. Pract., vol. 102, p. 104526, 2020.
[3] J. Li and X. Wang, “Altitude control of quadrotor UAV using improved PID control,” Aerosp. Sci. Technol., vol. 99, p. 105704, 2020.
[4] Q. Nguyen and J. Lee, “Genetic-algorithm-based PID optimization for drone systems,” Appl. Sci., vol. 11, no. 3, p. 1142, 2021.
[5] D. Kim and S. Lee, “Comparative performance of PD and PID controllers for UAV attitude stabilization,” Sensors, vol. 21, no. 12, p. 4179, 2021.
[6] A. Singh, “Disturbance rejection in UAVs using PID-based control,” Int. J. Control, vol. 93, no. 5, pp. 1045–1059, 2020.
[7] R. Kumar, et al., “Fuzzy adaptive PID for UAV flight control,” ISA Trans., vol. 120, pp. 183–192, 2022.
[8] I. Al-Mashaqbeh, “Neural-network tuning of PID controllers for UAVs,” IEEE Access, vol. 10, pp. 78512–78522, 2022.
[9] S. Rahman and K. Park, “Adaptive PD controllers for quadrotor attitude control,” Control Theory Technol., vol. 20, no. 4, pp. 514–528, 2022.
[10] M. Zhao and Y. Chen, “Hybrid PID–MPC control for UAV trajectory tracking,” Aerosp., vol. 10, no. 2, p. 211, 2023.
[11] J. Alvarez and P. Ruiz, “Neural PID control in nonlinear UAV systems,” J. Intell. Robot. Syst., vol. 108, no. 7, p. 411–426, 2023.
[12] Y. Tang, et al., “A comparative survey of classical and intelligent PID controllers in UAVs,” Appl. Sci., vol. 13, no. 5, p. 2905, 2023.
[13] T. Zhang, et al., “AI-enhanced PID controller design for quadrotors,” Robot. Auton. Syst., vol. 173, p. 104456, 2024.
[14] H. Zhang and J. Xu, “A review of PID control applications in UAVs,” Aerosp. Res. J., vol. 12, no. 1, pp. 65–78, 2024.
[15] P. Mohanty, “Performance analysis of PID tuning methods for UAV flight control,” IEEE Access, vol. 9, pp. 31211–31222, 2021.
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