Department of Mechanical Materials, Science and Technology College of Nanchang Hangkong University, Jiujiang 332020, China
| Abstract: | Purpose - Through a series of iterative processes, the proposed method allows the manipulator to get as close as possible to the desired path under joint constraints with high accuracy and short computation time. To meet the urgent need for accuracy and stability in the membrane wall welding process. Design/methodology/approach - This study focuses on the potential of model predictive control (MPC) to improve the ability of robotic systems to accomplish complex welding tasks. First, a gantry arm model suitable for membrane wall welding was developed for kinematic calculations. Extensive simulation tests were then conducted to investigate the accuracy and feasibility of MPC in accomplishing membrane wall welding tasks and its efficiency in resolving kinematic constraints. Finally, parameters such as trajectory accuracy and system responsiveness were also examined. Findings - The MPC approach significantly improved the trajectory tracking process for membrane wall welding, and the control system demonstrated high accuracy, efficiency, and adaptability, especially in nonlinear multiple-input. These multiple-output redundant robotic welding scenarios contribute to the development of more complex, accurate, and reliable robotic welding systems. Originality/Value - Through a series of iterative processes, the proposed method enables the gantry manipulator to get as close as possible to the desired paths under the welding structure and the motion degrees of freedom constraints, with high accuracy and fast convergence speed. Ethical Compliance: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. |
| Keywords: | Model Predictive Control; Membrane Wall Welding; CoppeliaSim; Trajectory Tracking Control; Gantry with Robot Arm |
| DOI: | 10.57237/j.mse.2026.02.001 |
| 1. | The Jiangxi Key Laboratory of Forming and Joining Technology for Aerospace Components (No: EL202280319) |
| 2. | Science and Technology Research Project of Jiangxi Provincial Department of Education (No: GJJ218716 and No: GJJ2204316) |
| 3. | Technology Research Project of Jiangxi Province Education Department (Grant No. S20231343310) |
| 4. | Nanchang Hangkong University Science and Technology College Student Innovation and Entrepreneurship Program (No: S20231343320) |
| [1] | Sun, D.-S., Lee, T.-E. & Kim, K.-H. (2005) Component allocation and feeder arrangement for a dual-gantry multi-head surface mounting placement tool. International Journal of Production Economics. 95(2), 245-264. https://doi.org/10.1016/j.ijpe.2004.01.003 |
| [2] | Wang, X., Xie, Z., Zhou, X., Gao, J., Li, F. & Gu, X. (2022) Adaptive path planning for the gantry welding robot system. Journal of Manufacturing Processes. 81, 386-395. https://doi.org/10.1016/j.jmapro.2022.07.005 |
| [3] | Wenna, W., Weili, D., Changchun, H., Heng, Z., Haibing, F. & Yao, Y. (2022) A digital twin for 3D path planning of large-span curved-arm gantry robot. Robotics and Computer-Integrated Manufacturing. 76, 102330. https://doi.org/10.1016/j.rcim.2022.102330 |
| [4] | Yang, X., Hu, H. & Cheng, C. (2023) Flexible yard space allocation plan for new type of automated container terminal equipped with unilateral-cantilever rail-mounted gantry cranes. Advanced Engineering Informatics. 58, 102193. https://doi.org/10.1016/j.aei.2023.102193 |
| [5] | Wang, X., Zhou, X., Xia, Z. & Gu, X. (2021) A survey of welding robot intelligent path optimization. Journal of Manufacturing Processes. 63, 14-23. https://doi.org/10.1016/j.jmapro.2020.04.085 |
| [6] | Wen, D., Yao, Y. S., Li, L., Jeon, M.-G., Zhou, Q. L., Li, N. & Deguchi, Y. (2019) Experimental study on the working states of membrane walls in the arch-fired boiler with different fuel proportion coefficients. Applied Thermal Engineering. 148, 404-411. https://doi.org/10.1016/j.applthermaleng.2018.10.098 |
| [7] | Yang, A., Chen, Y., Naeem, W., Fei, M. & Chen, L. (2021) Humanoid motion planning of robotic arm based on human arm action feature and reinforcement learning. Mechatronics. 78, 102630. https://doi.org/10.1016/j.mechatronics.2021.102630 |
| [8] | Kumar, S. A., Chand, R., Chand, R. P. & Sharma, B. (2023) Linear manipulator: Motion control of an n-link robotic arm mounted on a mobile slider. Heliyon. 9(1). https://doi.org/10.1016/j.heliyon.2023.e12867 |
| [9] | Golovin, I., Maksakov, A., Shysh, M. & Palis, S. (2022) Discrepancy-based control for positioning of large gantry crane. Mechanical Systems and Signal Processing. 163, 108199. https://doi.org/10.1016/j.ymssp.2021.108199 |
| [10] | Ji, X., Chen, Y. & Yang, S. (2022) Stud welding system using an industrial robot for membrane walls. The International Journal of Advanced Manufacturing Technology. 121(11), 8467-8477. https://doi.org/10.1007/s00170-022-09892-9 |
| [11] | Taysom, B. S., Sorensen, C. D. & Hedengren, J. D. (2017) A comparison of model predictive control and PID temperature control in friction stir welding. Journal of Manufacturing Processes. 29, 232-241. https://doi.org/10.1016/j.jmapro.2017.07.015 |
| [12] | Ma, L., Lou, X. & Jia, J. (2023) Neural-network-based boundary control for a gantry crane system with unknown friction and output constraint. Neurocomputing. 518, 271-281. https://doi.org/10.1016/j.neucom.2022.11.010 |
| [13] | Wang, X., Gao, J., Zhou, X. & Gu, X. (2024) Path Planning for the Gantry Welding Robot System Based on Improved RRT*. Robotics and Computer-Integrated Manufacturing. 85, 102643. https://doi.org/10.1016/j.rcim.2023.102643 |
| [14] | Awad, N., Lasheen, A., Elnaggar, M. & Kamel, A. (2022) Model predictive control with fuzzy logic switching for path tracking of autonomous vehicles. ISA Transactions. 129, 193-205. https://doi.org/10.1016/j.isatra.2021.12.022 |
| [15] | Liu, Z., Chang, G., Yuan, H., Tang, W., Xie, J., Wei, X. & Dai, H. (2023) Adaptive look-ahead model predictive control strategy of vehicular PEMFC thermal management. Energy. 285, 129176. https://doi.org/10.1016/j.energy.2023.129176 |
| [16] | Schwenzer, M., Ay, M., Bergs, T. & Abel, D. (2021) Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology. 117(5), 1327-1349. https://doi.org/10.1007/s00170-021-07682-3 |
| [17] | Brian Froisy, J. (1994) Model predictive control: Past, present and future. ISA Transactions. 33(3), 235-243. https://doi.org/10.1016/0019-0578(94)90095-7 |
| [18] | Lv, C., Wang, G. & Chen, H. (2020) Estimation of time-dependent thermal boundary conditions and online reconstruction of transient temperature field for boiler membrane water wall. International Journal of Heat and Mass Transfer. 147, 118955. https://doi.org/10.1016/j.ijheatmasstransfer.2019.118955 |
| [19] | Zhang, Z., Malashkhia, L., Zhang, Y., Shevtshenko, E. & Wang, Y. (2022) Design of Gaussian process based model predictive control for seam tracking in a laser welding digital twin environment. Journal of Manufacturing Processes. 80, 816-828. https://doi.org/10.1016/j.jmapro.2022.06.047 |
| [20] | García, C. E., Prett, D. M. & Morari, M. (1989) Model predictive control: Theory and practice—A survey. Automatica. 25 (3), 335-348. https://doi.org/10.1016/0005-1098(89)90002-2 |
| [21] | Qin, S. J. & Badgwell, T. A. (2003) A survey of industrial model predictive control technology. Control Engineering Practice. 11(7), 733-764. https://doi.org/10.1016/S0967-0661(02)00186-7 |
| [22] | Lancaster, J. F. (1973) Failures of boilers and pressure vessels: Their causes and prevention. International Journal of Pressure Vessels and Piping. 1(2), 155-170. https://doi.org/10.1016/0308-0161(73)90020-3 |
We invite active, qualified and high profile scientists and researchers to join as Editorial Board Members.
Join UsScholars with a strong interest in reviewing are invited to join the reviewer panel to ensure the quality of the research to be published.
Join Us