KaydedilenlerDerginizi ekleyin

Dergiler / Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi / 2023 / Cilt: 14 Sayı: 2

Model Predictive Trajectory Tracking Control of 2 DoFs SCARA Robot under External Force Acting to the Tip along the Trajectory

Sertaç Emre KARA
Osman YİĞİD
Murat ŞEN
Mesut HÜSEYİNOĞLU
Sayfa
325–332
Yayın Tarihi
2023-06-20
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

According to simulation studies, successful results were obtained.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The robot arms often follow a certain trajectory depending on the type of end effector with functions of spray-painting, arc welding, bonding or machining etc.
📊
Temel Bulgular & Veri:In this study, a Model Predictive Control (MPC) for trajectory tracking control of a 2 Degrees of Freedom (DoFs) Selective Compliant Assembly Robot Arm (SCARA) under an external force acting to the tip of the robot along the trajectory was performed.
💡
Sonuç & Katkı:According to simulation studies, successful results were obtained.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:2 DoFs SCARA robot · trajectory control · model predictive control

Özet

The robot arms often follow a certain trajectory depending on the type of end effector with functions of spray-painting, arc welding, bonding or machining etc. Therefore, trajectory-tracking control is a very important issue in robot arm applications. Also, the robot must be able to follow the determined trajectory stably under the influence of external forces or machining forces it encounters in its operations. In this study, a Model Predictive Control (MPC) for trajectory tracking control of a 2 Degrees of Freedom (DoFs) Selective Compliant Assembly Robot Arm (SCARA) under an external force acting to the tip of the robot along the trajectory was performed. The effectiveness of the MPC method used has been demonstrated by simulation applications. According to simulation studies, successful results were obtained.

Abstract

The robot arms often follow a certain trajectory depending on the type of end effector with functions of spray-painting, arc welding, bonding or machining etc. Therefore, trajectory-tracking control is a very important issue in robot arm applications. Also, the robot must be able to follow the determined trajectory stably under the influence of external forces or machining forces it encounters in its operations. In this study, a Model Predictive Control (MPC) for trajectory tracking control of a 2 Degrees of Freedom (DoFs) Selective Compliant Assembly Robot Arm (SCARA) under an external force acting to the tip of the robot along the trajectory was performed. The effectiveness of the MPC method used has been demonstrated by simulation applications. According to simulation studies, successful results were obtained.

🔖

Bu Makaleye Atıf Yap

KARA, S. E., YİĞİD, O., ŞEN, M., HÜSEYİNOĞLU, M. (2023). Model Predictive Trajectory Tracking Control of 2 DoFs SCARA Robot under External Force Acting to the Tip along the Trajectory. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, Cilt: 14 Sayı: 2, 325–332. https://ajindex.com/articles/962730

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (10)

  1. R. H. Middleton and G. C. Goodw1n, “Adaptive computed torque control for rigid link manipulations,” Syst. Control Lett., vol. 10, pp. 9–16, 1988
  2. S. W. Wijesoma and R. J. Richards, “Robust Trajectory Following of Robots Using Computed Torque Structures with VSS”, International Journal of Control, 52 (1990), 4, pp. 935-962 [3] Z. Yang, J. Wu, J. Mei, J. Gao, and T. Huang, “Mechatronic Model Based Computed Torque Control of a Parallel Manipulator,” Int. J. Adv. Robot. Syst., vol. 5, no. 1, pp. 123–128, 2008
  3. O. O. Obadina, M. Thaha, K. Althoefer, and M. H. Shaheed, “A Modified Computed Torque Control Approach for a Master-Slave Robot Manipulator System,” in Towards Autonomous Robotic Systems. TAROS 2018. Lecture Notes in Computer Science, vol. vol 10965, M. Giuliani, T. Assaf, and M. Giannaccini, Eds. Springer, Cham, 2018, pp. 28–39
  4. M. Saad, P. Bigras, L. Dessaint, and K. Al-Haddad, “Adaptive Robot Control Using Neural Networks”, IEEE Transaction on Industrial Electronics, Vol.41, No: 2. 1994
  5. M. Lee, and Y. Choi, “An Adaptive Neurocontroller Using RBFN for Robot Manipulators”, IEEE Transaction on Industrial Electronics, Vol.51, (2004)
  6. W. Sun and Y. Wang, “An Adaptive Fuzzy control for Robotic Manipulators”, International Conference on Control, Automation, Robotics and Vision, Kunming China, 1952-1956, (2004)
  7. S. H. Hsu and L. C. Fu, “Adaptive decentralized control of robot manipulators driven by current-fed induction motors”, IEEE/ASME Trans. Mechatronic, 10 (2005), 4, pp. 465-468
  8. J. Cronin, J. M. Escano, S. Roshany-Yamchi and N. Canty, “Fuzzy-Based Generalized Predictive Control of a Robotic Arm”, Proceedings, 25th IET Irish Signals & Systems Conference, 2014
  9. R. Wai, “Tracking Control Based on Neural Network Strategy for Robot Manipulator”, Neurocomputing, 51, 425-445, (2003)
  10. K. Lochan and B. K. Roy, “Control of Two-link 2-DOF Robot Manipulator Using Fuzzy Logic Techniques: A Review”, Proceedings, 4 th International Conference on Soft Computing for Problem Solving, Warsaw, Poland, 2014 [12] N. Mendes and P. Neto, “Indirect adaptive fuzzy control for industrial robots: a solution for contact applications”, Expert Systems with Applications, 42 (2015), 22, pp. 929-935 [13] W. He, Y. Chen and Z. Yin, “Adaptive neural network control of an uncertain robot with full-state constraints”, IEEE Trans. Cybern, 46 (2016), 3, pp. 620-629 [14] H. Chaudhary, V. Panwar, R. Prasad and N. Sukavanam, “Adaptive Neuro Fuzzy Based