Araştırma Makalesi
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Design and Experimental Validation of an Effective Controller for Autonomous Differential Wheeled Robots

Yıl 2025, Cilt: 15 Sayı: 1, 362 - 381, 15.03.2025
https://doi.org/10.31466/kfbd.1564085

Öz

This research focuses on the control of differential wheeled robots and aims to develop a new, practical controller inspired by the well-known PID controller. The primary challenge with traditional PID controllers lies in the difficulty of accurately optimizing the three key parameters (coefficients of Proportional, Integral and Derivative) through experimental trial and error. While numerical simulations offer solutions for parameter optimization, real-world factors such as friction losses, inconsistent current and voltage supply to the DC motors, and wheel slippage often prevent these optimized parameters from achieving the same effectiveness in physical systems. This study proposes a novel controller designed to address these challenges, simplifying the parameter tuning process to a single, easily adjustable variable. The new controller, while inspired by the PID approach, offers an effective alternative, overcoming the limitations posed by real-world conditions. It was implemented and tested on a differential wheeled robot specifically designed for this research. Experimental testing across various tasks demonstrated that the new controller provides stable and reliable control, making it a valuable alternative to traditional PID-based approaches.

Kaynakça

  • Abbas, I. A., & Mustafa, M. K. (2024). A review of adaptive tuning of PID-controller: Optimization techniques and applications. International Journal of Nonlinear Analysis and Applications, 15(2), 29-37. https://doi.org/10.22075/ijnaa.2023.21415.4024
  • Ali, M., Firdaus, A., Arof, H., Nurohmah, H., Suyono, H., Putra, D. & Muslim, M. (2021). The comparison of dual axis photovoltaic tracking system using artificial intelligence techniques. Iaes International Journal of Artificial Intelligence (Ij-Ai), 10(4), 901. https://doi.org/10.11591/ijai.v10.i4.pp901-909
  • Byeon, Y. J., Jang, M., & Kim, Y. (2025, January). Kinodynamic Modular Approach Local Trajectory Planner for Straightforward Motions of Differential Wheeled Mobile Robots. In 2025 IEEE/SICE International Symposium on System Integration (SII) (pp. 573-580). IEEE.
  • Carlucho, I., De Paula, M., & Acosta, G. G. (2019). Double Q-PID algorithm for mobile robot control. Expert Systems with Applications, 137, 292-307.
  • Chung, Y., Park, C., & Harashima, F. (2001). A position control differential drive wheeled mobile robot. IEEE Transactions on Industrial Electronics, 48(4), 853-863.
  • Díaz-García, G., Giraldo, L. F., & Jimenez-Leudo, S. (2021, October). Dynamics of a differential wheeled robot: control and trajectory error bound. In 2021 IEEE 5th Colombian Conference on Automatic Control (CCAC) (pp. 25-30). IEEE.
  • Gharghory, S. and Kamal, H. (2012). Optimal tuning of pid controller using adaptive hybrid particle swarm optimization algorithm. International Journal of Computers Communications & Control, 7(1), 101. https://doi.org/10.15837/ijccc.2012.1.1426
  • Jin, L. (2023). Research on two-stage semi-active isd suspension based on improved fuzzy neural network pid control. Sensors, 23(20), 8388. https://doi.org/10.3390/s23208388
  • Joseph, S. B., Dada, E. G., Abidemi, A., Oyewola, D. O., & Khammas, B. M. (2022). Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems. Heliyon, 8(5).
  • Kesavan, E., Gowthaman, N., Tharani, S., Manoharan, S., & Arunkumar, E. (2016). Design and implementation of internal model control and particle swarm optimization based pid for heat exchanger system. International Journal of Heat and Technology, 34(3), 386-390. https://doi.org/10.18280/ijht.340306
  • Klancar, G., Zdesar, A., Blazic, S., & Skrjanc, I. (2017). Wheeled mobile robotics: from fundamentals towards autonomous systems. Butterworth-Heinemann.
  • Lee, C. and Chen, R. (2015). Optimal self-tuning pid controller based on low power consumption for a server fan cooling system. Sensors, 15(5), 11685-11700. https://doi.org/10.3390/s150511685
  • Lee, W., Kim, T., Kim, J., & Seo, T. (2025). Differential-Driven Wheeled Mobile Robot Mechanism with High Step-Climbing Ability. IEEE Robotics and Automation Letters.
  • Li, B., Ji, Z., Zhao, Z., & Yang, C. (2025). Model Predictive Optimization and Terminal Sliding Mode Motion Control for Mobile Robot With Obstacle Avoidance. IEEE Transactions on Industrial Electronics.
  • Mohanty, P. K., & Parhi, D. R. (2013). Controlling the motion of an autonomous mobile robot using various techniques: a review. Journal of Advance Mechanical Engineering, 1(1), 24-39.
  • Mújica-Vargas, D., Vela-Rincón, V., Luna-Álvarez, A., Rendón-Castro, A., Matuz-Cruz, M., & Rubio, J. (2022). Navigation of a differential wheeled robot based on a type-2 fuzzy inference tree. Machines, 10(8), 660.
  • Ortenzi, V., Marturi, N., Mistry, M., & Kuo, J. (2018). Vision-based framework to estimate robot configuration and kinematic constraints. Ieee/Asme Transactions on Mechatronics, 23(5), 2402-2412. https://doi.org/10.1109/tmech.2018.2865758
  • Qu, S., He, T., & Zhu, G. (2023). Model-assisted online optimization of gain-scheduled pid control using nsga-ii iterative genetic algorithm. Applied Sciences, 13(11), 6444. https://doi.org/10.3390/app13116444
  • Raj, M. and Seamans, R. (2019). Primer on artificial intelligence and robotics. Journal of Organization Design, 8(1). https://doi.org/10.1186/s41469-019-0050-0
  • Sahu, P. and Prusty, R. (2018). Performance enhancement in agc of multi-area power system with woa optimized fo-2dof controller and facts controllers. International Journal of Engineering & Technology, 7(3.3), 562. https://doi.org/10.14419/ijet.v7i2.33.14835
  • Salman, G., Jafar, A., & Ismael, A. (2019). Application of artificial intelligence techniques for lfc and avr systems using pid controller. International Journal of Power Electronics and Drive Systems (Ijpeds), 10(3), 1694. https://doi.org/10.11591/ijpeds.v10.i3.pp1694-1704
  • Shah, P., & Agashe, S. (2016). Review of fractional PID controller. Mechatronics, 38, 29-41.
  • Somefun, O. A., Akingbade, K., & Dahunsi, F. (2021). The dilemma of PID tuning. Annual Reviews in Control, 52, 65-74.
  • Tahtawi, A., Putri, F., & Martin, M. (2023). Position control of ax-12 servo motor using proportional-integral-derivative controller with particle swarm optimization for robotic manipulator application. Iaes International Journal of Robotics and Automation (Ijra), 12(2), 184. https://doi.org/10.11591/ijra.v12i2.pp184-191
  • Ye, L., Hou, Y., & Li, D. (2017). Genetic algorithm’s application for optimization of pid parameters in dynamic positioning vessel. Matec Web of Conferences, 139, 00153. https://doi.org/10.1051/matecconf/201713900153
  • Zangeneh, M., Aghajari, E., & Forouzanfar, M. (2022). A review on optimization of fuzzy controller parameters in robotic applications. IETE Journal of Research, 68(6), 4150-4159.
  • Zhang, J. (2015). Improved decoupled nonminimal state space model based pid for multivariable processes. Industrial & Engineering Chemistry Research, 54(5), 1640-1645. https://doi.org/10.1021/ie504314c
  • Zhang, J., Liu, J., Liu, B., & Li, M. (2022). Fractional order pid control based on ball screw energy regenerative active suspension. Actuators, 11(7), 189. https://doi.org/10.3390/act11070189
  • Zhu, Z., Kaizu, Y., Furuhashi, K., & Imou, K. (2021). Visual-inertial RGB-D SLAM with encoders for a differential wheeled robot. IEEE Sensors Journal, 22(6), 5360-5371. https://doi.org/10.1109/JSEN.2021.3101370

Diferansiyel Tekerlekli Otonom Robotlar için Etkili bir Denetleyicinin Tasarımı ve Deneysel Doğrulaması

Yıl 2025, Cilt: 15 Sayı: 1, 362 - 381, 15.03.2025
https://doi.org/10.31466/kfbd.1564085

Öz

Bu araştırma, diferansiyel tekerlekli robotların denetimine odaklanmakta ve yaygın olarak bilinen PID denetleyiciden esinlenerek yeni ve pratik bir denetleyici geliştirmeyi amaçlamaktadır. Geleneksel PID denetleyicideki temel zorluk, deneysel deneme yanılma yoluyla üç temel parametrenin (Orantı, İntegral ve Türev işlevlerine ait katsayılar) doğru bir şekilde optimize edilmesinin güçlüğünde yatmaktadır. Sayısal benzetimler parametre optimizasyonu için çözümler sunarken, sürtünme kayıpları, DC motorlara tutarsız akım ve voltaj beslemesi ve tekerlek kayması gibi fiziksel gerçek faktörleri, bu optimize edilmiş parametrelerin gerçek hayatta aynı etkinliğe ulaşmasını sıklıkla engellemektedir. Bu çalışma, parametre optimizasyon sürecini tek ve kolayca ayarlanabilir bir değişkene basitleştirerek bu zorlukları ele almak üzere tasarlanmış yeni bir denetleyici önermektedir. PID yaklaşımından esinlenen yeni denetleyici, gerçek dünya koşullarının getirdiği sınırlamaların üstesinden gelerek etkili bir alternatif sunmaktadır. Önerilen denetleyici, bu araştırma için özel olarak tasarlanmış diferansiyel tekerlekli bir robot üzerinde uygulanmış ve test edilmiştir. Çeşitli görevlerde yapılan deneysel testler, yeni denetleyicinin istikrarlı ve etkili denetim sağladığını ve onu geleneksel PID tabanlı yaklaşımlara göre değerli bir alternatif haline getirdiğini göstermiştir.

Kaynakça

  • Abbas, I. A., & Mustafa, M. K. (2024). A review of adaptive tuning of PID-controller: Optimization techniques and applications. International Journal of Nonlinear Analysis and Applications, 15(2), 29-37. https://doi.org/10.22075/ijnaa.2023.21415.4024
  • Ali, M., Firdaus, A., Arof, H., Nurohmah, H., Suyono, H., Putra, D. & Muslim, M. (2021). The comparison of dual axis photovoltaic tracking system using artificial intelligence techniques. Iaes International Journal of Artificial Intelligence (Ij-Ai), 10(4), 901. https://doi.org/10.11591/ijai.v10.i4.pp901-909
  • Byeon, Y. J., Jang, M., & Kim, Y. (2025, January). Kinodynamic Modular Approach Local Trajectory Planner for Straightforward Motions of Differential Wheeled Mobile Robots. In 2025 IEEE/SICE International Symposium on System Integration (SII) (pp. 573-580). IEEE.
  • Carlucho, I., De Paula, M., & Acosta, G. G. (2019). Double Q-PID algorithm for mobile robot control. Expert Systems with Applications, 137, 292-307.
  • Chung, Y., Park, C., & Harashima, F. (2001). A position control differential drive wheeled mobile robot. IEEE Transactions on Industrial Electronics, 48(4), 853-863.
  • Díaz-García, G., Giraldo, L. F., & Jimenez-Leudo, S. (2021, October). Dynamics of a differential wheeled robot: control and trajectory error bound. In 2021 IEEE 5th Colombian Conference on Automatic Control (CCAC) (pp. 25-30). IEEE.
  • Gharghory, S. and Kamal, H. (2012). Optimal tuning of pid controller using adaptive hybrid particle swarm optimization algorithm. International Journal of Computers Communications & Control, 7(1), 101. https://doi.org/10.15837/ijccc.2012.1.1426
  • Jin, L. (2023). Research on two-stage semi-active isd suspension based on improved fuzzy neural network pid control. Sensors, 23(20), 8388. https://doi.org/10.3390/s23208388
  • Joseph, S. B., Dada, E. G., Abidemi, A., Oyewola, D. O., & Khammas, B. M. (2022). Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems. Heliyon, 8(5).
  • Kesavan, E., Gowthaman, N., Tharani, S., Manoharan, S., & Arunkumar, E. (2016). Design and implementation of internal model control and particle swarm optimization based pid for heat exchanger system. International Journal of Heat and Technology, 34(3), 386-390. https://doi.org/10.18280/ijht.340306
  • Klancar, G., Zdesar, A., Blazic, S., & Skrjanc, I. (2017). Wheeled mobile robotics: from fundamentals towards autonomous systems. Butterworth-Heinemann.
  • Lee, C. and Chen, R. (2015). Optimal self-tuning pid controller based on low power consumption for a server fan cooling system. Sensors, 15(5), 11685-11700. https://doi.org/10.3390/s150511685
  • Lee, W., Kim, T., Kim, J., & Seo, T. (2025). Differential-Driven Wheeled Mobile Robot Mechanism with High Step-Climbing Ability. IEEE Robotics and Automation Letters.
  • Li, B., Ji, Z., Zhao, Z., & Yang, C. (2025). Model Predictive Optimization and Terminal Sliding Mode Motion Control for Mobile Robot With Obstacle Avoidance. IEEE Transactions on Industrial Electronics.
  • Mohanty, P. K., & Parhi, D. R. (2013). Controlling the motion of an autonomous mobile robot using various techniques: a review. Journal of Advance Mechanical Engineering, 1(1), 24-39.
  • Mújica-Vargas, D., Vela-Rincón, V., Luna-Álvarez, A., Rendón-Castro, A., Matuz-Cruz, M., & Rubio, J. (2022). Navigation of a differential wheeled robot based on a type-2 fuzzy inference tree. Machines, 10(8), 660.
  • Ortenzi, V., Marturi, N., Mistry, M., & Kuo, J. (2018). Vision-based framework to estimate robot configuration and kinematic constraints. Ieee/Asme Transactions on Mechatronics, 23(5), 2402-2412. https://doi.org/10.1109/tmech.2018.2865758
  • Qu, S., He, T., & Zhu, G. (2023). Model-assisted online optimization of gain-scheduled pid control using nsga-ii iterative genetic algorithm. Applied Sciences, 13(11), 6444. https://doi.org/10.3390/app13116444
  • Raj, M. and Seamans, R. (2019). Primer on artificial intelligence and robotics. Journal of Organization Design, 8(1). https://doi.org/10.1186/s41469-019-0050-0
  • Sahu, P. and Prusty, R. (2018). Performance enhancement in agc of multi-area power system with woa optimized fo-2dof controller and facts controllers. International Journal of Engineering & Technology, 7(3.3), 562. https://doi.org/10.14419/ijet.v7i2.33.14835
  • Salman, G., Jafar, A., & Ismael, A. (2019). Application of artificial intelligence techniques for lfc and avr systems using pid controller. International Journal of Power Electronics and Drive Systems (Ijpeds), 10(3), 1694. https://doi.org/10.11591/ijpeds.v10.i3.pp1694-1704
  • Shah, P., & Agashe, S. (2016). Review of fractional PID controller. Mechatronics, 38, 29-41.
  • Somefun, O. A., Akingbade, K., & Dahunsi, F. (2021). The dilemma of PID tuning. Annual Reviews in Control, 52, 65-74.
  • Tahtawi, A., Putri, F., & Martin, M. (2023). Position control of ax-12 servo motor using proportional-integral-derivative controller with particle swarm optimization for robotic manipulator application. Iaes International Journal of Robotics and Automation (Ijra), 12(2), 184. https://doi.org/10.11591/ijra.v12i2.pp184-191
  • Ye, L., Hou, Y., & Li, D. (2017). Genetic algorithm’s application for optimization of pid parameters in dynamic positioning vessel. Matec Web of Conferences, 139, 00153. https://doi.org/10.1051/matecconf/201713900153
  • Zangeneh, M., Aghajari, E., & Forouzanfar, M. (2022). A review on optimization of fuzzy controller parameters in robotic applications. IETE Journal of Research, 68(6), 4150-4159.
  • Zhang, J. (2015). Improved decoupled nonminimal state space model based pid for multivariable processes. Industrial & Engineering Chemistry Research, 54(5), 1640-1645. https://doi.org/10.1021/ie504314c
  • Zhang, J., Liu, J., Liu, B., & Li, M. (2022). Fractional order pid control based on ball screw energy regenerative active suspension. Actuators, 11(7), 189. https://doi.org/10.3390/act11070189
  • Zhu, Z., Kaizu, Y., Furuhashi, K., & Imou, K. (2021). Visual-inertial RGB-D SLAM with encoders for a differential wheeled robot. IEEE Sensors Journal, 22(6), 5360-5371. https://doi.org/10.1109/JSEN.2021.3101370
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Osman Ünal 0000-0003-1101-6561

Yayımlanma Tarihi 15 Mart 2025
Gönderilme Tarihi 9 Ekim 2024
Kabul Tarihi 12 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 15 Sayı: 1

Kaynak Göster

APA Ünal, O. (2025). Design and Experimental Validation of an Effective Controller for Autonomous Differential Wheeled Robots. Karadeniz Fen Bilimleri Dergisi, 15(1), 362-381. https://doi.org/10.31466/kfbd.1564085