Welding Robot Controlled Using PSO-Fuzzy Technique
DOI:
https://doi.org/10.52716/jprs.v15i3.864Keywords:
Welding robot, Flexible joint robot, PID controller, Fuzzy logic control, Particle Swarm Optimization (PSO), Oil pipeline networks.Abstract
This work addressed the use of a robot arm with flexible joints as a welding robot for oil pipeline networks. One of the trickiest processes with strict quality criteria is welding oil pipelines. A highly skilled welder with considerable expertise is typically required. At the moment, robotics technology is sophisticated and is used in many technical applications. Robotics are highly precise workers; They operate with high precision and minimal error during their job implementations. In this paper, a classic PID controller is employed to control the welding robot arm movements since the Proportional-Integral-Derivative (PID) controller requires parameter tuning in the presence of any disturbance. Intelligent controllers are required, and for this purpose, a fuzzy logic controller is presented to improve the welding robot's performance during changing circumstances of operation. To optimize the fuzzy parameters, a particle swarm optimization method (PSO) is proposed to determine the selection of the optimal values of the fuzzy membership’s parameters. The simulation results show that the suggested controller has high performance during welding, even in the presence of disturbances.
References
Robotiq, “Welding Robot Teaching”, [Online]. Available: http://robotiq.com/applications/robot-teaching/
B.-S. Ryuh and G. R., "Arc Welding Robot Automation Systems", Industrial Robotics: Programming, Simulation and Applications, Pro Literatur Verlag, Germany / ARS, Austria, Dec. 01, 2006. http://dx.doi.org/10.5772/4918.
J. Norberto Pires, A. Loureiro, T. Godinho, P. Ferreira, B. Fernando and J. Morgado, "Welding robots", in IEEE Robotics & Automation Magazine, vol. 10, no. 2, pp. 45-55, June 2003. https://doi.org/10.1109/MRA.2003.1213616.
S. M. Dawood, S. H. Majeed, and H. J. Nekad, "PID controller based multiple (master/slaves) permanent magnet synchronous motors speed control", Iraqi Journal of Electrical and Electronic Engineering, vol. 11, no. 2, pp. 183-192, 2015.
S. M. Dawood, R. Z. Homod, and A. Hatami, "Optimizing HVAC&R System Efficiency and Comfort Levels Using Machine Learning-Based Control Methods", Tikrit Journal of Engineering Sciences, vol. 32, no. 2, pp. 1–20, 2025. https://doi.org/10.25130/tjes.32.2.25.
S. M. Dawood, A. Hatami, and R. Z. Homod, "Trade-off decisions in a novel deep reinforcement learning for energy savings in HVAC systems", Journal of Building Performance Simulation, vol. 15, no. 6, pp. 809–831, 2022. https://doi.org/10.1080/19401493.2022.2099465.
S. B. Suslick, D. Schiozer, and M. R. Rodriguez, “Uncertainty and Risk Analysis in Petroleum Exploration and Production”, Terrae, vol. 6, no. 1, pp. 30-41. 2009.
S. M. Swadi, A. I. Majeed, and A. A. Ugla, “Design and Simulation of Robotic Arm PD Controller Based on PSO”, University of Thi-Qar Journal for Engineering Sciences, vol. 10, no. 1, pp. 18–24, Jun. 2019. https://doi.org/10.31663/tqujes.10.1.311(2019).
P. Kah, M. Shrestha, E. Hiltunen, and J. Martikainen, “Robotic arc welding sensors and programming in industrial applications”, International Journal of Mechanical and Materials Engineering, vol. 10, no. 1, p. 13, 2015. https://doi.org/10.1186/s40712-015-0042-y.
M. A. Rashidifar, A. A. Rashidifar, and D. Ahmadi, “Modeling and Control of 5DOF Robot Arm Using Fuzzy Logic Supervisory Control”, International Journal of Robotics and Automation (IJRA), vol. 2, no. 2, pp. 56–68, 2013. http://doi.org/10.11591/ijra.v2i2.pp56-68.
M. M. Zirkohi, M. M. Fateh, and M. A. Shoorehdeli, “Type-2 Fuzzy Control for a Flexible-joint Robot Using Voltage Control Strategy”, International Journal of Automation and Computing, vol. 10, no. 3, pp. 242–255, 2013. https://doi.org/10.1007/s11633-013-0717-x.
S. Butdee and J. Thanomsin, “Materials Today : Proceedings Robotic welding using fuzzy logic to predict penetration for an oil pipeline weldment”, Materials Today: Proceedings, vol. 26, part 2, pp. 2425-2431, 2020. https://doi.org/10.1016/j.matpr.2020.02.517.
Y. I. Zhang, J. U. N. Xiao, Z. Zhang, and H. U. A. Dong, “Intelligent Design of Robotic Welding Process Parameters Using Learning-Based Methods”, IEEE Access, vol. 10, pp. 13442–13450, 2022. https://doi.org/10.1109/ACCESS.2022.3146404
Z. Lin, M. Yue, G. Chen, and J. Sun, “Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles”, Transactions of the Institute of Measurement and Control, vol. 44, no. 1, pp. 121-132, 2021. https://doi.org/10.1177/01423312211024798.
N. Rokbani, B. Neji, M. Slim, S. Mirjalili, and R. Ghandour, “applied sciences A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm”, Applied Sciences, vol. 12, no. 14, p. 7091, 2022. https://doi.org/10.3390/app12147091.
M. Vijay and D. Jena, “PSO based neuro fuzzy sliding mode control for a robot manipulator”, Journal of Electrical Systems and Information Technology, vol. 4, no. 1, pp. 243-256, 2016. https://doi.org/10.1016/j.jesit.2016.08.006
A. J. Attiya, Y. Wenyu, and S. W. Shneen, “Compared with PI , Fuzzy-PI and PSO-PI Controllers of Robotic Grinding Force Servo System”, TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 16, no. 1, pp. 65–74, 2015. http://doi.org/10.11591/tijee.v16i1.1589.
V. N. Babu and A. Srisailaja, “Particle Swarm Optimization Based Tuning of PID Controller for Robot Arm Joint Control”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 5, no. 6, pp. 5421–5428, 2016. http://doi.org/10.15662/IJAREEIE.2016.0506117.
H. G. Kamil and A. A. Ahmed, and A. K. Abbas, “Tuning of Control Motion for a three link robot manipulator using Particle Swarm Optimization Technique”, Journal University of Kerbala, vol. 15, no. 4, pp. 102–110, 2017.
X.-L. Li, R. Serra, and J. Olivier, “Effects of Particle Swarm Optimization Algorithm Parameters for Structural Dynamic Monitoring of Cantilever Beam”, Surveillance, Vishno and AVE conferences, Lyon, France, p. hal-02188562, 2019.
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