Optimal Design of PID Controller Parameters with Particle Swarm Optimization for Quadrotors
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i4.1347Keywords:
quadrotor control, PID controller tuning, Particle Swarm Optimisation, unmanned aerial vehicle, control de cuadricópteros, ajuste de controladores PID, optimización por enjambre de partículasAbstract
This study presents a comprehensive comparative analysis of multiple Particle Swarm Optimization configurations for optimizing Proportional-Integral-Derivative controllers in quadrotor unmanned aerial vehicles. Traditional PID tuning methods such as Ziegler-Nichols often produce suboptimal performance for complex multi-variable systems. We investigate four distinct PSO configurations—Fast, Balanced, Quality, and Aggressive—evaluating their performance across five diverse flight scenarios including basic takeoff, attitude control, and transitional flight maneuvers. The optimization process focuses on twelve PID parameters controlling altitude, roll, pitch, and yaw dynamics. Experimental results demonstrate that PSO-based optimization significantly outperforms conventional Ziegler-Nichol’s tuning, with Root Mean Square Error improvements ranging from 45.2% to 51.7% across different configurations. Statistical analysis confirms the robustness and consistency of PSO-based approaches across varying operational conditions.
Spanish-language metadata / Metadatos en español
Título en español:
Diseño óptimo de los parámetros de controladores PID mediante optimización por enjambre de partículas para cuadricópteros
Resumen:
Este estudio presenta un análisis comparativo exhaustivo de múltiples configuraciones de optimización por enjambre de partículas (PSO) para optimizar controladores proporcional-integral-derivativo (PID) en vehículos aéreos no tripulados de tipo cuadricóptero. Los métodos tradicionales de ajuste PID, como el de Ziegler–Nichols, suelen producir un desempeño subóptimo en sistemas multivariables complejos. Se investigan cuatro configuraciones distintas de PSO —Fast, Balanced, Quality y Aggressive— y se evalúa su desempeño en cinco escenarios de vuelo diferentes, entre ellos el despegue básico, el control de actitud y las maniobras de vuelo transicional. El proceso de optimización se centra en doce parámetros PID que controlan la dinámica de altitud, alabeo, cabeceo y guiñada. Los resultados experimentales demuestran que la optimización basada en PSO supera significativamente al ajuste convencional de Ziegler–Nichols, con mejoras en la raíz del error cuadrático medio (RMSE) que oscilan entre el 45.2 % y el 51.7 % para las distintas configuraciones. El análisis estadístico confirma la robustez y la consistencia de los enfoques basados en PSO bajo diversas condiciones operativas.
Palabras Claves:
control de cuadricópteros; ajuste de controladores PID; optimización por enjambre de partículas; control multivariable; vehículo aéreo no tripulado; dinámica de vuelo; optimización evolutiva; minimización del RMSE; control de actitud; control de altitud; ajuste de Ziegler–Nichols; evaluación del desempeño del controlador.
Smart citations:
https://scite.ai/reports/10.61467/2007.1558.2026.v17i4.1347
Dimensions.
Open Alex.
References
Ahmed, N. M. A., & Saleh, M. H. (2018). Tri-copter drone modeling with PID control tuned by PSO algorithm. International Journal of Computer Applications, 181(25), 46–52. https://doi.org/10.5120/ijca2018918060
Ang, K. H., Chong, G., & Li, Y. (2005). PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13(4), 559–576. https://doi.org/10.1109/TCST.2005.847331
Bayisa, A. T., & Geng, L.-H. (2019). Controlling quadcopter altitude using PID-control system. International Journal of Engineering Research & Technology, 8(12), 195–199. https://doi.org/10.17577/IJERTV8IS120118
Cárdenas, J. A., Carrero, U. E., Camacho, E. C., & Calderón, J. M. (2022). Optimal PID φ axis control for UAV quadrotor based on multi-objective PSO. IFAC-PapersOnLine, 55(14), 101–106. https://doi.org/10.1016/j.ifacol.2022.07.590
Eiben, A. E., & Smith, J. E. (2015). Introduction to evolutionary computing (2nd ed.). Springer. https://doi.org/10.1007/978-3-662-44874-8
El Gmili, N., Mjahed, M., El Kari, A., & Ayad, H. (2019). Particle swarm optimization and cuckoo search-based approaches for quadrotor control and trajectory tracking. Applied Sciences, 9(8), Article 1719. https://doi.org/10.3390/app9081719
Engelbrecht, A. P. (2007). Computational intelligence: An introduction (2nd ed.). Wiley. https://doi.org/10.1002/9780470512517
Gaing, Z.-L. (2004). A particle swarm optimization approach for optimum design of PID controller in AVR systems. IEEE Transactions on Energy Conversion, 19(2), 384–391. https://doi.org/10.1109/TEC.2003.821821
Gomez, V., Gomez, N., Rodas, J., Paiva, E., Saad, M., & Gregor, R. (2020). Pareto optimal PID tuning for Px4-based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm. Aerospace, 7(6), Article 71. https://doi.org/10.3390/aerospace7060071
Kang, C., Park, B., & Choi, J. (2022). Scheduling PID attitude and position control frequencies for time-optimal quadrotor waypoint tracking under unknown external disturbances. Sensors, 22(1), Article 150. https://doi.org/10.3390/s22010150
Karahan, M., & Kasnakoglu, C. (2019). Modeling and simulation of quadrotor UAV using PID controller. In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1–4). IEEE. https://doi.org/10.1109/ECAI46879.2019.9042043
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95—International Conference on Neural Networks (Vol. 4, pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
Li, J., & Li, Y. (2011). Dynamic analysis and PID control for a quadrotor. In 2011 IEEE International Conference on Mechatronics and Automation (pp. 573–578). IEEE. https://doi.org/10.1109/ICMA.2011.5985724
Luque-Vega, L. F., Castillo-Toledo, B., & Loukianov, A. G. (2012). Robust block second order sliding mode control for a quadrotor. Journal of the Franklin Institute, 349(2), 719–739. https://doi.org/10.1016/j.jfranklin.2011.10.017
Mahony, R., Kumar, V., & Corke, P. (2012). Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor. IEEE Robotics & Automation Magazine, 19(3), 20–32. https://doi.org/10.1109/MRA.2012.2206474
Nazaruddin, Y. Y., Andrini, A. D., & Anditio, B. (2018). PSO based PID controller for quadrotor with virtual sensor. IFAC-PapersOnLine, 51(4), 358–363. https://doi.org/10.1016/j.ifacol.2018.06.091
Ogata, K. (2010). Modern control engineering (5th ed.). Prentice Hall.
Salamat, B., & Tonello, A. M. (2019). Adaptive nonlinear PID control for a quadrotor UAV using particle swarm optimization. In 2019 IEEE Aerospace Conference (pp. 1–12). IEEE. https://doi.org/10.1109/AERO.2019.8741829
Sheta, A., Braik, M., Maddi, D. R., Mahdy, A., Aljahdali, S., & Turabieh, H. (2021). Optimization of PID controller to stabilize quadcopter movements using meta-heuristic search algorithms. Applied Sciences, 11(14), Article 6492. https://doi.org/10.3390/app11146492
Tran, H.-K., & Chiou, J.-S. (2016). PSO-based algorithm applied to quadcopter micro air vehicle controller design. Micromachines, 7(9), Article 168. https://doi.org/10.3390/mi7090168
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
