SavedAdd your journal

Journals / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2025 / Volume: 14 Issue: 4

Trajectory Tracking of a Mobile Robot with GWO-Based Type-II Fuzzy Logic Controller

Tayfun Abut(MUŞ ALPARSLAN ÜNİVERSİTESİ)0000-0003-4646-3345
Enver Salkım(MUS ALPARSLAN UNIVERSITY)0000-0002-7342-8126
Pages
—
Publication Date
2025-12-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Simulation results indicate that the PID controller exhibits significant deviations, particularly during sharp turns and sudden maneuvers in square and star trajectories, leading to increased tracking errors.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study addresses the trajectory tracking problem of wheeled mobile robots and proposes two different control strategies, which are comparatively evaluated through simulations.
📊
Key Findings & Data:Simulation results indicate that the PID controller exhibits significant deviations, particularly during sharp turns and sudden maneuvers in square and star trajectories, leading to increased tracking errors.
💡
Conclusion & Impact:Overall, the results highlight the effectiveness of the suggested control method in enhancing position control accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Mobile Robot · Type II Fuzzy Logic Control · PID Controller

Abstract

This study addresses the trajectory tracking problem of wheeled mobile robots and proposes two different control strategies, which are comparatively evaluated through simulations. Initially, the mathematical model of the mobile robot is derived, followed by the design of two controllers: a conventional Proportional-Integral-Derivative (PID) controller and a Type-II Fuzzy Logic Controller (Type-II FLC) optimized utilizing the Grey Wolf Optimizer (GWO) algorithm. The suggested control methods are assessed under three different reference trajectory scenarios—circle, square, and star-shaped paths. Simulation results indicate that the PID controller exhibits significant deviations, particularly during sharp turns and sudden maneuvers in square and star trajectories, leading to increased tracking errors. In contrast, the GWO-based Type-II FLC demonstrates smoother and more stable maneuvers, resulting in lower tracking errors and higher trajectory-following accuracy. These findings suggest that the GWO-optimized Type-II FLC enables more reliable and precise navigation of mobile robots in dynamic environments contrasted to the classical PID controller. Furthermore, the Type-II FLC method achieved considerable percentage improvements in tracking performance for circular, square, and star trajectories when contrasted with the PID controller. Specifically, in a square orbit, the X and Y position errors have been improved by 98% and 97%, respectively, according to the Type II FLC, PID control method. Overall, the results highlight the effectiveness of the suggested control method in enhancing position control accuracy.

🔖

Cite This Article

Abut, T., Salkım, E. (2025). Trajectory Tracking of a Mobile Robot with GWO-Based Type-II Fuzzy Logic Controller. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 14 Issue: 4. https://doi.org/10.17798/bitlisfen.1753970

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (44)

  1. L. Caracciolo, A. De Luca, and S. Iannitti, “Trajectory tracking control of a four-wheel differentially driven mobile robot,” in Proc. IEEE Int. Conf. Robot. Autom., vol. 4, Detroit, MI, USA, May 1999, pp. 2632–2638, doi: 10.1109/robot.1999.773994.
  2. K. Kozłowski and D. Pazderski, “Modeling and control of a 4-wheel skid-steering mobile robot,” Int. J. Appl. Math. Comput. Sci., vol. 14, no. 4, pp. 477–496, 2004.
  3. E. Maalouf, M. Saad, and H. Saliah, “A higher level path tracking controller for a four-wheel differentially steered mobile robot,” Robot. Auton. Syst., vol. 54, no. 1, pp. 23–33, 2006, doi: 10.1016/j.robot.2005.10.001.
  4. U. Kumar and N. Sukavanam, “Backstepping based trajectory tracking control of a four wheeled mobile robot,” Int. J. Adv. Robot. Syst., vol. 5, no. 4, p. 38, 2008, doi: https://doi.org/10.5772/6224.
  5. C. Cariou, R. Lenain, B. Thuilot, and M. Berducat, “Automatic guidance of a four-wheel-steering mobile robot for accurate field operations,” J. Field Robot., vol. 26, no. 6–7, pp. 504–518, 2009, doi: 10.1002/rob.20282.
  6. M. H. Lee and T. H. S. Li, “Kinematics, dynamics and control design of 4WIS4WID mobile robots,” J. Eng., vol. 2015, no. 1, pp. 6–16, 2015, doi: 10.1049/joe.2014.0241.
  7. N. B. Hoang and H. J. Kang, “Neural network-based adaptive tracking control of mobile robots in the presence of wheel slip and external disturbance force,” Neurocomputing, vol. 188, pp. 12–22, 2016, doi: 10.1016/j.neucom.2015.02.101.
  8. J. Liao, Z. Chen, and B. Yao, “Performance-oriented coordinated adaptive robust control for four-wheel independently driven skid steer mobile robot,” IEEE Access, vol. 5, pp. 19048–19057, 2017, doi:10.1109/ACCESS.2017.2754647.
  9. M. Deremetz et al., “Path tracking of a four-wheel steering mobile robot: A robust off-road parallel steering strategy,” in Proc. Eur. Conf. Mobile Robots (ECMR), Paris, France, Sep. 2017, pp. 1–7, doi: 10.1109/ECMR.2017.8098670.
  10. J. Liao, Z. Chen, and B. Yao, “Model-based coordinated control of four-wheel independently driven skid steer mobile robot with wheel–ground interaction and wheel dynamics,” IEEE Trans. Ind. Inf., vol. 15, no. 3, pp. 1742–1752, 2018, doi: 10.1109/TII.2018.2869573.
  11. Q. Qiu et al., “Extended Ackerman steering principle for the coordinated movement control of a four-wheel drive agricultural mobile robot,” Comput. Electron. Agric., vol. 152, pp. 40–50, 2018, doi: 10.1016/j.compag.2018.06.036.
  12. I. Zeidis and K. Zimmermann, “Dynamics of a four-wheeled mobile robot with Mecanum wheels,” ZAMM J. Appl. Math. Mech., vol. 99, no. 12, pp. e201900173, 2019, doi: 10.1002/zamm.201900173.
  13. T. Abut and M. Huseyinoğlu, “Modeling and optimal trajectory tracking control of wheeled a mobile robot,” Caucasian J. Sci., vol. 6, no. 2, pp. 137–146, 2019.
  14. L. Jiang et al., “Anti-disturbance direct yaw moment control of a four-wheeled autonomous mobile robot,” IEEE Access, vol. 8, pp. 174654–174666, 2020, doi: 10.1109/ACCESS.2020.3025575.
  15. X. Zhang et al., “Motion planning and tracking control of a four-wheel independently driven steered mobile robot with multiple maneuvering modes,” Front. Mech. Eng., vol. 16, no. 3, pp. 504–527, 2021, doi: 10.1007/s11465-020-0626-y.
  16. S. Raikwar, J. Fehrmann, and T. Herlitzius, “Navigation and control development for a four-wheel-steered mobile orchard robot using model-based design,” Comput. Electron. Agric., vol. 202, p. 107410, 2022, doi: 10.1016/j.compag.2022.107410.
  17. M. Szeremeta and M. Szuster, “Neural tracking control of a four-wheeled mobile robot with mecanum wheels,” Appl. Sci., vol. 12, no. 11, p. 5322, 2022, doi: 10.3390/app12115322.
  18. J. Qu et al., “Performance analysis and optimization for steering motion mode switching of an agricultural four-wheel-steering mobile robot,” Agronomy, vol. 12, no. 11, p. 2655, 2022, doi: 10.3390/agronomy12112655.
  19. B. Bae and D. H. Lee, “Design of a four-wheel steering mobile robot platform and adaptive steering control for manual operation,” Electronics, vol. 12, no. 16, p. 3511, 2023, doi: 10.3390/electronics12163511.
  20. J. Zhang et al., “Variable gain based composite trajectory tracking control for 4-wheel skid-steering mobile robots with unknown disturbances,” Control Eng. Pract., vol. 132, p. 105428, 2023, doi: 10.1016/j.conengprac.2022.105428.
  21. S. Ryu, J. Won, and T. Seo, “Simulation study on four-wheeled mobile robot mechanisms using various performance criteria,” Robot. Auton. Syst., vol. 179, p. 104749, 2024, doi: 10.1016/j.robot.2024.104749.
  22. L. Zhang et al., “An integral terminal sliding mode-based adaptive control approach for traversing unknown inclined surfaces,” Robot. Auton. Syst., vol. 186, p. 104928, 2025, doi: 10.1016/j.robot.2025.104928.
  23. Y. H. Chen, “Nonlinear adaptive fuzzy hybrid sliding mode control design for trajectory tracking of autonomous mobile robots,” Mathematics, vol. 13, no. 8, p. 1329, 2025, doi: 10.3390/math13081329.
  24. M. Cipriano, G. Oriolo, and A. Cherubini, “Singularity-free trajectory tracking for steerable wheeled mobile robots,” IEEE Robot. Autom. Lett., 2025, doi: 10.1109/LRA.2025.3564209.
  25. G. Shadrin et al., “Application of compensation algorithms to control the speed and course of a four-wheeled mobile robot,” Sensors, vol. 24, no. 22, p. 7233, 2024, doi: 10.3390/s24227233.
  26. L. Bruzzone, S. E. Nodehi, and P. Fanghella, “WheTLHLoc 4W: Small-scale inspection ground mobile robot with two tracks, two rotating legs, and four wheels,” J. Field Robot., vol. 41, no. 4, pp. 1146–1166, 2024, doi: 10.1002/rob.22314.
  27. S. Amertet, G. Gebresenbet, and H. M. Alwan, “Optimizing the performance of a wheeled mobile robots for use in agriculture using a linear-quadratic regulator,” Robot. Auton. Syst., vol. 174, p. 104642, 2024, doi: 10.1016/j.robot.2024.104642ç
  28. T. Lan and G. Yang, “Four-wheeled mobile robot with flexible posture control,” J. Field Robot., vol. 42, no. 4, pp. 1287–1297, 2025, doi: 10.1002/rob.22450.
  29. D. Seo and J. Kang, “Controller design for active four-wheel steering and four-wheel independent drive-based mobile robot: Enhancing cornering performance in negative phase,” Int. J. Control Autom. Syst., vol. 23, no. 1, pp. 235–248, 2025, doi: 10.1007/s12555-024-0631-8.
  30. J. Yi, D. Song, J. Zhang, and Z. Goodwin, “Adaptive trajectory tracking control of skid-steered mobile robots,” in Proc. IEEE Int. Conf. Robot. Autom., Rome, Italy, Apr. 2007, pp. 2605–2610, doi: 10.1109/ROBOT.2007.363858.
  31. S. Arslan and H. Temeltaş, “Robust motion control of a four wheel drive skid-steered mobile robot,” in Proc. Int. Conf. Electr. Electron. Eng. (ELECO), Bursa, Turkey, Dec. 2011, pp. II-415.
  32. C. C. Hang, K. J. Åström, and W. K. Ho, “Refinements of the Ziegler–Nichols tuning formula,” IEE Proc. D, Control Theory Appl., vol. 138, no. 2, pp. 111–118, 1991, doi: 10.1049/ip-d.1991.0015.
  33. L. A. Zadeh, “Fuzzy logic,” Computer, vol. 21, no. 4, pp. 83–93, 1988, doi: 10.1109/2.53.
  34. L. A. Zadeh, G. J. Klir, and B. Yuan, Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems. Singapore: World Scientific, 1996.
  35. L. A. Zadeh, “The role of fuzzy logic in the management of uncertainty in expert systems,” Fuzzy Sets Syst., vol. 11, no. 1–3, pp. 199–227, 1983, doi: 10.1016/S0165-0114(83)80081-5.
  36. N. N. Karnik, J. M. Mendel, and Q. Liang, “Type-2 fuzzy logic systems,” IEEE Trans. Fuzzy Syst., vol. 7, no. 6, pp. 643–658, 1999, doi: 10.1109/91.811231.
  37. J. M. Mendel, R. I. John, and F. Liu, “Interval type-2 fuzzy logic systems made simple,” IEEE Trans. Fuzzy Syst., vol. 14, no. 6, pp. 808–821, 2006, doi: 10.1109/TFUZZ.2006.879986.
  38. T. Abut, E. Salkim, and H. Tugal, “Active suspension control for improved ride comfort and vehicle performance using HHO-based type-I and type-II fuzzy logic,” Biomimetics, vol. 10, no. 10, p. 673, 2025, doi: 10.3390/biomimetics10100673.
  39. J. R. Castro, O. Castillo, and P. Melin, “An interval type-2 fuzzy logic toolbox for control applications,” in Proc. IEEE Int. Fuzzy Syst. Conf., London, U.K., Jul. 2007, pp. 1–6, doi: 10.1109/FUZZY.2007.4295341.
  40. S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014, doi: 10.1016/j.advengsoft.2013.12.007.
  41. T. Abut, E. Salkim, and A. Demosthenous, “Performance improvement in a vehicle suspension system with FLQG and LQG control methods,” Actuators, vol. 14, no. 3, 2025, doi: 10.3390/act14030137.
  42. H. Faris, I. Aljarah, M. A. Al-Betar, and S. Mirjalili, “Grey wolf optimizer: A review of recent variants and applications,” Neural Comput. Appl., vol. 30, pp. 413–435, 2018, doi: 10.1007/s00521-017-3272-5.
  43. T. Abut, “Optimal LQR controller methods for double inverted pendulum system on a cart,” Dicle Univ. J. Eng., vol. 14, no. 2, pp. 247–255, 2023, doi: 10.24012/dumf.1253331.
  44. T. Abut, “An inverted pendulum system control with fuzzy linear quadratic regulator method: Experimental validation,” Comput., Mater. Continua, vol. 85, no. 2, pp. 4023–4042, 2025, doi: 10.32604/cmc.2025.066920.