Robust Control of a Quadcopter BLDC Motor: Comparative Analysis of PID and H∞ Controllers

Year 2025, Volume: 9 Issue: Special Issue 1st Future of Vehicles: Innovation, Engineering and Economic Conference, 1 - 6

Barnabás Kiss , Aron Ballagi , Miklós Kuczmann

Abstract

The aim of the present study was to investigate a control strategy designed for the BLDC (Brushless Direct Current) motor of a quadcopter-type drone, with particular emphasis on the precise and stable maintenance of altitude in a disturbed environment. Two different control methods were implemented and compared during the research: the classical PID (Proportional–Integral–Derivative) controller and the H∞ (H-infinity) control technique. The investigations were carried out along two approaches. On the one hand, a transfer function—reproduced from a previous study—was used as a possible reference, and tested in the MATLAB simulation environment. On the other hand, a custom-developed physical prototype with one degree of freedom was created, capable of vertical motion along a single axis, allowing for the examination of altitude control under real-world conditions. The purpose of the system was to maintain a predetermined hovering altitude even in the presence of external disturbances, such as artificially generated wind. During the design of the control algorithms, a state-space-based modeling approach was applied, and appropriate weighting functions were defined, with special attention given to robustness against disturbances, control accuracy, and energy-efficient operation. The simulation results showed that the H∞ controller reduced average power demand by 19.43% compared to PID control, while practical measurements demonstrated a 38% decrease in average power consumption. In addition, overshoot was reduced by 96% and oscillation amplitude by 86% under wind disturbance. The objective of the research was to examine the practical applica-bility of an advanced control method that can provide greater stability, reliability, and energy effi-ciency under varying environmental conditions compared to traditional solutions.

Keywords

Altitude Control , Custom-built Drone Model , Energy Efficiency , H-infinity Control , PID Control , State-space Modeling

References

• [1] Ezhil VS, Sriram BR, Vijay RC, Yeshwant S, Sabareesh RK, Dakkshesh G, Raffik R. Investigation on PID controller usage on Unmanned Aerial Vehicle for stability control. Mater Today Proc. 2022;66:1313–1318. https://doi.org/10.1016/j.matpr.2022.05.134
• [2] Lopez-Sanchez I, Moreno-Valenzuela J. PID control of quadrotor UAVs: A survey. Annu Rev Control. 2023;56:100900. https://doi.org/10.1016/j.arcontrol.2023.100900
• [3] Shi M. Application of PID Control Technology in Unmanned Aerial Vehicles. Appl Comput Eng. 2024;96:24–30. https://doi.org/10.54254/2755-2721/96/20241327
• [4] Mien TL, Tu TN, Van An V. Cascade PID Control for Altitude and Angular Position Stabilization of 6-DOF UAV Quadcopter. Int J Robot Control Syst. 2024;4(2). https://doi.org/10.31763/ijrcs.v4i2.1410
• [5] Yoon J, Doh J. Optimal PID control for hovering stabilization of quadcopter using long short term memory. Adv Eng Inform. 2022;53:101679. https://doi.org/10.1016/j.aei.2022.101679
• [6] Noordin A, Mohd Basri MA, Mohamed Z. Real-time implementation of an adaptive pid controller for the quadrotor mav embedded flight control system. Aerospace. 2023;10(1):59. https://doi.org/10.3390/aerospace10010059
• [7] Amertet S, Gebresenbet G, Alwan HM. Modeling of unmanned aerial vehicles for smart agriculture systems using hybrid fuzzy PID controllers. Appl Sci. 2024;14(8):3458. https://doi.org/10.3390/app14083458
• [8] Da Fonseca JEDN, Valente LCL, de Oliveira Evald PJD. Optimizing PID Controllers of Drone-based Wind Turbine Inspection Systems using the African Vulture Algorithm. Presented at the 2024 16th Seminar on Power Electronics and Control (SEPOC); 2024 Oct; Brazil. IEEE; 2024. p. 1–6. https://doi.org/10.1109/SEPOC63090.2024.10747459
• [9] Dakua SC, Pati BB. PIλDμ controllers for suppression of limit cycle in a plant with time delay and backlash nonlinearity. International Journal of Automotive Science and Technology. 2024;8(4):506–526. https://doi.org/10.30939/ijastech..1471847
• [10] Dam QT, Haidar F. Adaptive PID controller design for velocity control of a hydrogen internal combustion engine using RBF neural networks. Engineering Perspective. 2024;5(1):41–48. https://doi.org/10.29228/eng.pers.76280
• [11] Say MFQ, Sybingco E, Bandala AA, Vicerra RRP, Chua AY. A genetic algorithm approach to PID tuning of a quadcopter UAV model. Presented at the 2021 IEEE/SICE International Symposium on System Integration (SII); 2021 Jan; Iwaki, Japan. IEEE; 2021. p. 675–678. https://doi.org/10.1109/IEEECONF49454.2021.9382697
• [12] Ma'arif A, Setiawan NR. Control of DC motor using integral state feedback and comparison with PID: simulation and arduino implementation. J Robot Control (JRC). 2021;2(5):456–461. https://doi.org/10.18196/jrc.25122
• [13] Çelik İ, Arslan TA, Aysal FE, Bayrakçeken H, Oğuz Y. Comparison of control methods for half-car active suspension system. International Journal of Automotive Science and Technology. 2024;8(4):439–450. https://doi.org/10.30939/ijastech..1578123
• [14] Okasha, M., Kralev, J., & Islam, M. (2022). Design and experimental comparison of pid, lqr and mpc stabilizing controllers for parrot mambo mini-drone. Aerospace, 9(6), 298. https://doi.org/10.3390/aerospace9060298
• [15] Okulski, M., & Ławryńczuk, M. (2022). How much energy do we need to fly with greater agility? Energy consumption and performance of an attitude stabilization controller in a quadcopter drone: A modified MPC vs. PID. Energies, 15(4), 1380. https://doi.org/10.3390/en15041380
• [16] Asignacion Jr, A., & Satoshi, S. (2024). Historical and current landscapes of autonomous quadrotor control: An early-career researchers’ guide. Drones, 8(3), 72. https://doi.org/10.3390/drones8030072
• [17] Hui, N., Guo, Y., Han, X., & Wu, B. (2024, October). Robust H-Infinity Dual Cascade MPC-Based Attitude Control Study of a Quadcopter UAV. In Actuators (Vol. 13, No. 10). https://doi.org/10.3390/act13100392
• [18] Haidar F, Laib A, Ajwad SA, Guilbert G. Integrated Vehicle Dynamics Modeling, Path tracking, and Simulation: A MATLAB Implementation Approach. Engineering Perspective. 2024;4(1):7–16. https://doi.org/10.29228/eng.pers.75106
• [19] Ahmed AH, Ouda AN, Kamel AM, Elhalwagy YZ. Design and analysis of quadcopter classical controller. Presented at the International Conference on Aerospace Sciences and Aviation Technology (ASAT-16); 2015 May 26–28; Cairo, Egypt. The Military Technical College; 2015. p. 1–17. http://doi.org/10.21608/asat.2015.23032
• [20] Pila AW. Lagrangian Dynamics. In: Introduction To Lagrangian Dynamics. Cham: Springer International Publishing; 2019. p. 63–162. https://doi.org/10.1007/978-3-030-22378-6_3
• [21] Kulsinskas A, Durdevic P, Ortiz-Arroyo D. Internal wind turbine blade inspections using UAVs: Analysis and design issues. Energies. 2021;14(2):294. https://doi.org/10.3390/en14020294
• [22] Liu R, Liu Y, Zhang Y. State-Space Method-Based Frame Dynamics Analysis of the Six-Rotor Unmanned Aerial Vehicles. World Electr Veh J. 2025;16(6):331. https://doi.org/10.3390/wevj16060331
• [23] Kiss B, Ballagi Á, Kuczmann M. Investigation of Energy-Efficient UAV Control: Analysis of PID and MPC Performance. Accepted for publication in Eng Proc. To be presented at the Sustainable Mobility and Transportation Symposium; 2025 Oct 16–18; Győr, Hungary.