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

Year 2023, Volume: 14 Issue: 2, 325 - 332, 20.06.2023

Sertaç Emre Kara , Osman Yiğid , Murat Şen , Mesut Hüseyinoğlu

https://doi.org/10.24012/dumf.1289356

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.

Keywords

2 DoFs, SCARA robot, trajectory control, model predictive control

References

• [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.
• [4] 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.
• [5] 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.
• [6] M. Lee, and Y. Choi, “An Adaptive Neurocontroller Using RBFN for Robot Manipulators”, IEEE Transaction on Industrial Electronics, Vol.51, (2004).
• [7] 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).
• [8] 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.
• [9] 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.
• [10] R. Wai, “Tracking Control Based on Neural Network Strategy for Robot Manipulator”, Neurocomputing, 51, 425-445, (2003).
• [11] 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 Hybrid Force/Position Control for an Industrial Robot Manipulator”, Journal of Intelligent Manufacturing, 27 (2016), 6, pp. 1299-1308
• [15] M. Huseyinoglu and T. Abut, “Dynamic model and control of 2-dof robotic arm”, European Journal of Technique (EJT), 8(2), 141-150. 2018.
• [16] S. Choi, and J. Kim, “A Fuzzy-Sliding Mode Controller for Robust Tracking of Robotic Manipulators”, Mechatronics, Vol.7, 199-216, (1997).
• [17] A. M. Mustafa, “Modeling, simulation and control of 2-R robot”, Global Journals of Research in Engineering, 14(H1), 49-54. 2014
• [18] J. P. Perez, R. Soto, A. Flores, F. Rodriguez, and J. L. Meza, “Trajectory Tracking Error Using PID Control Law for Two-Link Robot Manipulator via Adaptive Neural Networks”, Procedia Technology, vol. 3, pp. 139–146, 2012. https://doi.org/10.1016/j.protcy.2012.03.015
• [19] Q. Ma and X. Lei, “Dynamic Path Planning of Mobile Robots Based on ABC Algorithm”, Lecture Notes in Computer Science, pp. 267–274, 2010. https://doi.org/10.1007/978-3-642-16527-6_34
• [20] P. V. Savsani and R. L. Jhala, “Optimal Motion Planning For a Robot Arm by Using Artificial Bee Colony (ABC) Algorithm”, International Journal of Modern Engineering Research (IJMER), vol. 2, no. 6, pp. 4434–4438, 2012.
• [21] M. Aydin and O. Yakut, “Real-time control of triglide robot using sliding mode control method”, Industrial Robot: An International Journal, 45(1), 89-97, 2018.
• [22] S. Aksungur, M. Aydin and O. Yakut, “Real-time PID control of a novel RCM mechanism designed and manufactured for use in laparoscopic surgery”, Industrial Robot: An International Journal, 47(2), 153-166, 2020.
• [23] T. Abut and S. Soyguder, “Real-time control of bilateral teleoperation system with adaptive computed torque method”, Industrial Robot: An International Journal, 44(3), 299-311, 2017.
• [24] T. Abut and S. Soyguder, “Optimal adaptive computed torque control for haptic-teleoperation system with uncertain dynamics”, Proceedings of the Institution of Mechanical Engineers Part I: Journal of Systems and Control Engineering, 236(4), 800-817, 2022.
• [25] C. E. Garcia, D. M. Prett and M. Morari, “Model Predictive Control: Theory and Practice a Survey”, Automatica, Vol. 25, No. 3, pp. 335-3t8, 1989.
• [26] M. Nauman, W. Shireen and A. Hussain, “Model-Free Predictive Control and Its Applications”, Energies, 15, 5131 2022 https://doi.org/10.3390/en15145131.
• [27] S. Qina and T. A. Badgwell, “A survey of industrial model predictive control technology”, Control Engineering Practice 11 733–764, 2003
• [28] L. A. Zadeh, and B. H. Whalen, “On optimal control and linear programming”, IRE Trans. Aut. Control, 7(4), 45, 1962.
• [29] A. I. Propoi, “Use of LP methods for synthesizing sampled-data automatic systems”, Automn Remote Control, 24, 837, 1963.
• [30] R. K. Mehra, R. Rouhani, J. Eterno, J. Richalet and A. Rault, “Model algorithmic control: review and recent development”. Engng Foundation Conf. on Chemical Process Control II, Sea Island, Georgia, pp. 287-310, 1982.
• [31] D. M. Prett and R. D.Gillette, “Optimization and constrained multivariable control of a catalytic cracking unit”. AIChE National Mtg, Houston, Texas; also Proc. Joint Aut. Control Conf., San Francisco, California, 1979.
• [32] P. E. Orukpe, “Model Predictive Control Fundamentals”, Nigerian Journal of Technology, Vol. 31, No. 2, pp. 139-148, 2012.
• [33] Z. Houzhong, L. Jiasheng, Y. Chaochun, S. Xiaoqiang and C. Yingfeng, “Application of explicit model predictive control to a vehicle semi-active suspension system”, Journal of Low Frequency Noise, Vibration and Active Control, 39(3) 772–786, 2020.
• [34] S. S. Oyelere, “The Application of Model Predictive Control (MPC) to Fast Systems such as Autonomous Ground Vehicles (AGV)”, Journal of Computer Engineering, 16(3), 27-37, 2014.
• [35] G. B. Avanzini, A. M. Zanchettin and P. Rocco “Reactive Constrained Model Predictive Control for Redundant Mobile Manipulators”, Advances in Intelligent Systems and Computing book series (AISC,volume 302).
• [36] E. H. Guechi, S. Bouzoualegh, Y. Zennir and S. Bllazic, “MPC Control and LQ Optimal Control of A Two-Link Robot Arm: A Comparative Study”, Machines, 6, 37, 2018.