Araştırma Makalesi
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Design and Implementation of an Optimized PID Controller for Two-Limb Robot Arm Control

Yıl 2024, , 192 - 204, 24.03.2024
https://doi.org/10.17798/bitlisfen.1370223

Öz

With developing technology, robot arms are used in more areas, and this is directly proportional to the work done for its development. Studies on robot arms are generally focused on control. The controllability of robot arms generally provides speed and precision.
Within the scope of this study, the control optimization of a two-arm robot arm with an optimized proportional integral differential controller (PID) was carried out using a microcontroller. The kinematic operations required to control a two-arm robot arm have been developed by the MATLAB Support Package for Arduino Hardware. The transfer function required for the control system was used for a direct current (DC) brushed motor, using the values given in the motor data sheet. Feedback is provided for the control system thanks to the Hall effect encoder.
The gripper end of the two-limbed robot arm follows the specified square-shaped reference. In this study, where PID controller was used, controller parameters were obtained with particle swarm, artificial bee colony and chaos game metaheuristic optimization algorithms for square orbit and these parameters were used on the produced robot arm.
Many methods have been used in the literature to determine PID parameters. In this study, the chaos game metaheuristic optimization algorithm, which has become popular in recent years, was used to determine the parameters of the PID controller.

Kaynakça

  • [1] W. S. Barbosa, M. M. Gioia, V. G. Natividade, R. F. Wanderley, M. R. Chaves, F. C. Gouvea and F. M. Gonçalves, “Industry 4.0: examples of the use of the robotic arm for digital manufacturing processes,” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 14, pp.1569-1575, 2020.
  • [2] S. K. Valluru and M. Singh, “Optimization strategy of bio-inspired metaheuristic algorithms tuned PID controller for PMBDC actuated robotic manipulator,” Procedia Computer Science, vol.171, pp. 2040-2049, 2020.
  • [3] K. J. Åström and T. Hägglund. “The future of PID control,” Control engineering practice, vol. 9, no. 11, pp.1163-1175, 2001.
  • [4] S. B. Joseph, E. G. Dada, A. Abidemi, D. O. Oyewola, and B. M. Khammas, “Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems,” Heliyon, vol. 8, no. 5, p.e09399, 2022.
  • [5] S. Kucuk and Z. Bıngul, Robot kinematics: Forward and inverse kinematics, London, UK: INTECH Open Access Publisher, 2006, pp. 117-148.
  • [6] S. Maheriya and P. Parikh, “A review: Modelling of Brushed DC motor and Various type of control methods,” Journal for Research, vol. 1, 2016.
  • [7] X. Hu, “Particle swarm optimization tutorial, titan.csit.rmit.edu, 2006. [Online]”. Available: https://titan.csit.rmit.edu.au/ e46507/publications/pso-tutorial-seal06.pdf. [Accessed: March 22, 2021].
  • [8] J. Tang, G. Liu and Q. Pan, “A review on representative swarm intelligence algorithms for solving optimization problems,” Applications and trends. IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 10, pp.1627-1643, 2021.
  • [9] E. Boğar, “Optimizasyon kuramında yeni bir metasezgisel yaklaşım: Ergen kimlik arama algoritması (AISA) ve mühendislik uygulamaları,” Doktora tezi, Pamukkale Üniversitesi Fen bilimleri Enstitüsü, Denizli, 2021.
  • [10] R. Eberhart and J. Kennedy, “Particle swarm optimization,” in Proceedings of the IEEE international conference on neural networks vol. 4, pp. 1942-1948, November 1995.
  • [11] D. Bratton and J. Kennedy, “Defining a standard for particle swarm optimization,” In 2007 IEEE swarm intelligence symposium, pp. 120-127, April 2007.
  • [12] M. Azab, “Global maximum power point tracking for partially shaded PV arrays using particle swarm optimization,” International Journal of Renewable Energy Technology, vol. 1, no.2, pp. 211-235, 2009.
  • [13] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of global optimization, vol. 39, pp. 459-471, 2007.
  • [14] H. O. Erkol, “Ters sarkaç sisteminin yapay arı kolonisi algoritması ile optimizasyonu,” Politeknik Dergisi, vol. 20, no. 4, pp.863-868, 2017.
  • [15] S. Hassan, B. Abdelmajid, Z. Mourad, S. Aicha and B. Abdenaceur, “An advanced MPPT based on artificial bee colony algorithm for MPPT photovoltaic system under partial shading condition. International,” Journal of Power Electronics and Drive Systems, vol. 8, no. 2, p. 647, 2017.
  • [16] S. Talatahari and M . Azizi, “Chaos game optimization: a novel metaheuristic algorithm,” Artificial Intelligence Review, vol. 54, no. 2 pp. 917-1004, 2021.
  • [17] M. Barakat, “Novel chaos game optimization tuned-fractional-order PID fractional-order PI controller for load–frequency control of interconnected power systems,” Protection and Control of Modern Power Systems, vol. 7, no. 1, 2022.
Yıl 2024, , 192 - 204, 24.03.2024
https://doi.org/10.17798/bitlisfen.1370223

Öz

Kaynakça

  • [1] W. S. Barbosa, M. M. Gioia, V. G. Natividade, R. F. Wanderley, M. R. Chaves, F. C. Gouvea and F. M. Gonçalves, “Industry 4.0: examples of the use of the robotic arm for digital manufacturing processes,” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 14, pp.1569-1575, 2020.
  • [2] S. K. Valluru and M. Singh, “Optimization strategy of bio-inspired metaheuristic algorithms tuned PID controller for PMBDC actuated robotic manipulator,” Procedia Computer Science, vol.171, pp. 2040-2049, 2020.
  • [3] K. J. Åström and T. Hägglund. “The future of PID control,” Control engineering practice, vol. 9, no. 11, pp.1163-1175, 2001.
  • [4] S. B. Joseph, E. G. Dada, A. Abidemi, D. O. Oyewola, and B. M. Khammas, “Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems,” Heliyon, vol. 8, no. 5, p.e09399, 2022.
  • [5] S. Kucuk and Z. Bıngul, Robot kinematics: Forward and inverse kinematics, London, UK: INTECH Open Access Publisher, 2006, pp. 117-148.
  • [6] S. Maheriya and P. Parikh, “A review: Modelling of Brushed DC motor and Various type of control methods,” Journal for Research, vol. 1, 2016.
  • [7] X. Hu, “Particle swarm optimization tutorial, titan.csit.rmit.edu, 2006. [Online]”. Available: https://titan.csit.rmit.edu.au/ e46507/publications/pso-tutorial-seal06.pdf. [Accessed: March 22, 2021].
  • [8] J. Tang, G. Liu and Q. Pan, “A review on representative swarm intelligence algorithms for solving optimization problems,” Applications and trends. IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 10, pp.1627-1643, 2021.
  • [9] E. Boğar, “Optimizasyon kuramında yeni bir metasezgisel yaklaşım: Ergen kimlik arama algoritması (AISA) ve mühendislik uygulamaları,” Doktora tezi, Pamukkale Üniversitesi Fen bilimleri Enstitüsü, Denizli, 2021.
  • [10] R. Eberhart and J. Kennedy, “Particle swarm optimization,” in Proceedings of the IEEE international conference on neural networks vol. 4, pp. 1942-1948, November 1995.
  • [11] D. Bratton and J. Kennedy, “Defining a standard for particle swarm optimization,” In 2007 IEEE swarm intelligence symposium, pp. 120-127, April 2007.
  • [12] M. Azab, “Global maximum power point tracking for partially shaded PV arrays using particle swarm optimization,” International Journal of Renewable Energy Technology, vol. 1, no.2, pp. 211-235, 2009.
  • [13] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of global optimization, vol. 39, pp. 459-471, 2007.
  • [14] H. O. Erkol, “Ters sarkaç sisteminin yapay arı kolonisi algoritması ile optimizasyonu,” Politeknik Dergisi, vol. 20, no. 4, pp.863-868, 2017.
  • [15] S. Hassan, B. Abdelmajid, Z. Mourad, S. Aicha and B. Abdenaceur, “An advanced MPPT based on artificial bee colony algorithm for MPPT photovoltaic system under partial shading condition. International,” Journal of Power Electronics and Drive Systems, vol. 8, no. 2, p. 647, 2017.
  • [16] S. Talatahari and M . Azizi, “Chaos game optimization: a novel metaheuristic algorithm,” Artificial Intelligence Review, vol. 54, no. 2 pp. 917-1004, 2021.
  • [17] M. Barakat, “Novel chaos game optimization tuned-fractional-order PID fractional-order PI controller for load–frequency control of interconnected power systems,” Protection and Control of Modern Power Systems, vol. 7, no. 1, 2022.
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kontrol Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Said Müftü 0000-0001-5621-7805

Barış Gökçe 0000-0001-6141-7625

Erken Görünüm Tarihi 21 Mart 2024
Yayımlanma Tarihi 24 Mart 2024
Gönderilme Tarihi 2 Ekim 2023
Kabul Tarihi 8 Mart 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

IEEE S. Müftü ve B. Gökçe, “Design and Implementation of an Optimized PID Controller for Two-Limb Robot Arm Control”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 13, sy. 1, ss. 192–204, 2024, doi: 10.17798/bitlisfen.1370223.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

Bitlis Eren Üniversitesi Lisansüstü Eğitim Enstitüsü        
Beş Minare Mah. Ahmet Eren Bulvarı, Merkez Kampüs, 13000 BİTLİS        
E-posta: fbe@beu.edu.tr