Analysis and Tuning of PID and Fuzzy Logic Controllers for a DC Motor-Driven Quadcopter Drone Altitude Control System
DOI:
https://doi.org/10.67151/rujbas.v1i1.40Keywords:
Quadcopter drone, PID controller, FLC controller, altitude control, DC motor modeling, MATLAB/simulinkAbstract
Quadcopter drones require precise and robust control systems to maintain stable flight under varying operating conditions. This article presented a modeling, design, tuning, and comparative evaluation of proportional-integrator-derivative (PID) and fuzzy logic controllers (FLC) for a DC-powered quadcopter altitude control system. Unlike simplified models that only consider altitude, the proposed approach integrated the entire control chain, including DC motor dynamics, propeller thrust generation, and the drone's vertical movement. The mathematical model of the motor was derived from electrical and mechanical principles, while the nonlinear thrust generation process was linearized around the hovering point. The PID controller was tuned for fast response, low over- oscillation, and zero steady-state error. To enhance robustness against nonlinear effects and distortions, a Mamdani-type fuzzy logic controller with two inputs and seven membership functions per variable was developed. MATLAB simulations were performed to compare transient response, frequency response, tracking accuracy, and interference rejection performance. The results found that the PID controller provided faster response (0.96 sec versus 1.53 sec for FLC), but exhibited higher overshoot (23.35% versus 12.81% for FLC). Both controllers achieved near-zero steady-state error (PID: 1.00001 rad/s, FLC: 0.9842 rad/s), while the fuzzy logic controller provided superior robustness and more stable operation against external interferences.
References
[1] Mohsan SAH, Othman NQH, Li Y, Alsharif MH, Khan MA. Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends. Intelligent Service Robotics. 2023;16(1):109-137. https://doi.org/10.1007/s11370-022-00452-4 DOI: https://doi.org/10.1007/s11370-022-00452-4
[2] Zulu A, John S. A review of control algorithms for autonomous quadrotors. Open Journal of Applied Sciences. 2014;4(14):547-556. http://dx.doi.org/10.4236/ojapps.2014.414053 DOI: https://doi.org/10.4236/ojapps.2014.414053
[3] Ang KH, Chong G, Li Y. PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology. 2005;13(4):559-576. https://doi.org/10.1109/TCST.2005.847331 DOI: https://doi.org/10.1109/TCST.2005.847331
[4] Hu N. The limitations of traditional PID controllers and modern optimization methods. Proceedings of the 3rd International Conference on Mechatronics and Smart Systems. 2025:238-244. https://doi.org/10.54254/2755-2721/2025.22912 DOI: https://doi.org/10.54254/2755-2721/2025.22912
[5] Isdaryani F, Shidiq MN, Wicaksono Y. Flight controller design for altitude control of a quadcopter using PID and fuzzy methods. Jurnal Teknologi Rekayasa. 2022;7(2):251-258. https://doi.org/10.31544/jtera.v7.i2.2022.251-258 DOI: https://doi.org/10.31544/jtera.v7.i2.2022.251-258
[6] Bouhabza N, Kara K. Optimized sliding mode based PID controller for a quadrotor system. 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE); 2022 Oct 29-31. Constantine, Algeria: IEEE; 2022. p. 1-6. https://doi.org/10.1109/ICAEE53772.2022.9962024 DOI: https://doi.org/10.1109/ICAEE53772.2022.9962024
[7] Castillo P, Lozano R, Dzul AE. Modelling and control of mini-flying machines. London: Springer-Verlag; 2015. https://doi.org/10.1007/1-84628-179-2 DOI: https://doi.org/10.1007/1-84628-179-2
[8] Åström KJ, Hägglund T. Advanced PID Control. Research Triangle Park, NC: ISA – Instrumentation, Systems, and Automation Society; 2006.
[9] O'Dwyer A. Handbook of PI and PID controller tuning rules. 3rd ed. London: Imperial College Press; 2009. DOI: https://doi.org/10.1142/p575
[10] Hoffmann GM, Huang H, Waslander SL, Tomlin CJ. Quadrotor helicopter flight dynamics and control: theory and experiment. In: AIAA Guidance, Navigation and Control Conference and Exhibit; 2007 Aug 20-23. Reston, VA: American Institute of Aeronautics and Astronautics; 2007. https://doi.org/10.2514/6.2007-6461 DOI: https://doi.org/10.2514/6.2007-6461
[11] Waslander SL, Wang C. Wind disturbance estimation and rejection for quadrotor position control. In: AIAA Infotech@Aerospace Conference; 2009 Apr 6-9. Reston, VA: American Institute of Aeronautics and Astronautics; 2009. https://doi.org/10.2514/6.2009-1983 DOI: https://doi.org/10.2514/6.2009-1983
[12] Zadeh LA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics. 1973;SMC-3(1):28-44. https://doi.org/10.1109/TSMC.1973.5408575 DOI: https://doi.org/10.1109/TSMC.1973.5408575
[13] Coza C, Macnab CJB. A new robust adaptive-fuzzy control method applied to quadrotor helicopter stabilization. In: NAFIPS 2006 – 2006 Annual Meeting of the North American Fuzzy Information Processing Society; 2006 Jun 3-6. Montreal, QC, Canada: IEEE; 2006. p. 454-8. https://doi.org/10.1109/NAFIPS.2006.365452 DOI: https://doi.org/10.1109/NAFIPS.2006.365452
[14] Amrouche R, Fas ML, Benrabah M, Ghoul A. Adaptive TLBO-based fuzzy logic controller for quadrotor trajectory tracking. IEEE Access. 2026;14:66711-66727. https://doi.org/10.1109/ACCESS.2026.3688552 DOI: https://doi.org/10.1109/ACCESS.2026.3688552
[15] Abbas NH, Sami AR. Tuning of PID controllers for quadcopter system using hybrid memory based gravitational search algorithm - particle swarm optimization. International Journal of Computer Applications. 2017;172(4):9-18. DOI: https://doi.org/10.5120/ijca2017915125
[16] Ojo KE, Adegbola OA, Aborisade DO. Comparative of Ziegler Nichols, fuzzy logic and extremum seeking based proportional integral derivative controller for quadcopter unmanned aerial vehicle stability control. Journal of Electrical, Control and Technological Research. 2021;3:1-10. DOI: https://doi.org/10.37121/jectr.vol3.161
[17] Koch W, Mancuso R, West R, Bestavros A. Reinforcement learning for UAV attitude control. Transactions on Cyber-Physical Systems. 2019;3(2):22. https://doi.org/10.1145/3301273 DOI: https://doi.org/10.1145/3301273
[18] Bou-Ammar H, Voos H, Ertel W. Controller design for quadrotor UAVs using reinforcement learning. In: 2010 IEEE International Conference on Control Applications; 2010 Sep 8-10. Yokohama, Japan: IEEE; 2010. p. 2130-5. https://doi.org/10.1109/CCA.2010.5611206 DOI: https://doi.org/10.1109/CCA.2010.5611206
[19] Xia CL. Permanent magnet brushless DC motor drives and controls. John Wiley & Sons Singapore Pte. Ltd.; 2012. DOI: https://doi.org/10.1002/9781118188347
[20] Kose O, Oktay T. Dynamic modeling and simulation of quadrotor for different flight conditions. European Journal of Science and Technology. 2019;15:132-42. https://doi.org/10.31590/ejosat.507222 DOI: https://doi.org/10.31590/ejosat.507222
[21] Mustapa MZ. Altitude controller design for quadcopter UAV. Jurnal Teknologi (Science & engineering). 2015;74(1):181-8. DOI: https://doi.org/10.11113/jt.v74.3993
[22] Ahmad F, Kumar P, Bhandari A, Patil PP. Simulation of the quadcopter dynamics with LQR based control. Materials Today Proceedings. 2020; 25(part 2): 326-32. https://doi.org/10.1016/j.matpr.2020.04.282 DOI: https://doi.org/10.1016/j.matpr.2020.04.282
[23] Priyambodo TK, Dhewa OA, Susanto T. Model of linear quadratic regulator (LQR) control system in waypoint flight mission of flying wing UAV. Journal of Telecommunication, Electronic and Computer Engineering. 2020;12(4):43-9.
[24] Abbas NH, Sami AR. Tuning of PID controllers for quadcopter system using cultural exchange imperialist competitive algorithm. Journal of Engineering. 2018;24(2):80-99. https://doi.org/10.31026/j.eng.2018.02.06 DOI: https://doi.org/10.31026/j.eng.2018.02.06
[25] Indrawati V, Prayitno A, Kusuma TA. Waypoint navigation of AR.Drone quadrotor using fuzzy logic controller. Telkomnika. 2015;13(3):381–91. DOI: https://doi.org/10.12928/telkomnika.v13i3.1862
[26] Fnu VS, Cohen K. Intelligent fuzzy flight control of an autonomous quadrotor UAV. In: 52nd Aerospace Sciences Meeting; 2014 Jan 13-17. National Harbor, Maryland: AIAA; 2014. https://doi.org/10.2514/6.2014-0992 DOI: https://doi.org/10.2514/6.2014-0992
[27] Rodríguez-Abreo O, Rodríguez-Reséndiz J, García-Cerezo A, García-Martínez JR. Fuzzy logic controller for UAV with gains optimized via genetic algorithm. Heliyon. 2024;10(4):e26363. https://doi.org/10.1016/j.heliyon.2024.e26363 DOI: https://doi.org/10.1016/j.heliyon.2024.e26363
[28] Beloglazov DA, Kobersi IS, Kosenko EY, Solovyev VV, Shadrina VV. Analysis of application aspects of regulators in quadrotor automatic control. Engineering Journal of Don. 2015;3:3078.
[29] Indrawati V, Prayitno A, Utomo G. Comparison of two fuzzy logic controller schemes for position control of AR.Drone. In: 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE); 2015 Oct 29-30. Chiang Mai, Thailand: IEEE; 2015. p. 360-3. https://doi.org/10.1109/ICITEED.2015.7408972 DOI: https://doi.org/10.1109/ICITEED.2015.7408972
[30] Polat O, Sezgin A. Position control of a quadcopter with PID and fuzzy-PID controller. Journal of Engineering Sciences and Design. 2024;12(1):34-48. https://doi.org/10.21923/jesd.1223998 DOI: https://doi.org/10.21923/jesd.1223998
[31] Vadakkepat P, Chong TC, Arokiasami WA, Weinan X. Fuzzy logic controllers for navigation and control of AR.Drone using Microsoft Kinect. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE); 2016 Jul 24-29. Vancouver, BC, Canada: IEEE; 2016. p. 856-63. https://doi.org/10.1109/FUZZ-IEEE.2016.7737778 DOI: https://doi.org/10.1109/FUZZ-IEEE.2016.7737778
Downloads
Published
Issue
Section
License
Copyright (c) 2026 2026 The Authors. Published by Ar-Rasheed Smart University, Yemen
This work is licensed under a Creative Commons Attribution 4.0 International License.
