It is one of the key tasks for the bridge crane to achieve anti-swing control of the hook and the accurate positioning of the body to work efficiently, safely and automatically. Based on Lagrange equation, this paper is to propose a dynamic model of the crane motion system for designing controller. In the controller design, Proportional-Integral-Derivative (PID), the most widely used controller in engineering, is adopted and a new parameter tuning algorithm for a multi-variable PID controller based on generalized predictive control (GPC) is given. It is found that the multi-variable PID controller shares the same structural mathematical expressions with the GPC law, which makes for the transfer and calculation of the three parameters P, I and D, and that the new algorithm enables the traditional PID controller to perform as brilliantly as the GPC. The results of both the simulation and real-time control experiments show that the newly-proposed PID controller can effectively eliminate the swing of the hook and control the bridge cranes moving position accurately.
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Z. Sun, N. Wang, Y. Bi, and J. Zhao, “A DE based PID controller for two-dimensional overhead crane,” Proceedings of the 34th Chinese Control Conference, Hangzhou, China, pp. 2546–2550, 2015.
X. Wu and X. He, “Enhanced damping-based anti-swing control method for underactuated overhead cranes,” IET Control Theory & Applications, vol. 9, no. 12, pp. 1893–1900, 2015.
S. Y. S. Hussien, R. Ghazali, H. I. Jaafar, and C. C. Soon, “An experimental of 3D gantry crane system in motion control by PID and PD controller via PFPSO optimization journal of telecommunication,” Electronic and Computer Engineering, vol. 8, no. 7, pp. 133–137, 2016.
M. J. Maghsoudi, Z. Mohamed, A. R. Husain, and M. O. Tokhi, “An optimal performance control scheme for a 3D crane,” Mechanical Systems and Signal Processing, vol. 66, no. 67, pp. 756–768, 2016.
L. X. Hai, T. H. Nguyen, T. G. Khanh, N. T. Thanh, B. T. Duong, and P. X. Minh, “Anti-sway tracking control of overhead crane system based on PID and fuzzy sliding mode control,” Journal of Science and Technology, vol. 55, no. 1, pp. 116–127, 2017.
M. Giacomelli, M. Faroni, D. Gorni, A. Marini, L. Simoni, and A. Visioli, “MPC-PID control of operator-in-the-loop overhead cranes: a practical approach,” Proceedings of the 7th International Conference on Systems and Control, Spain, pp. 321–326, 2018.
Z. Sun, Y. Bi, X. Zhao, Z. Sun, C. Ying, and S. Tan, “Type-2 fuzzy sliding mode anti-swing controller design and optimization for overhead crane,” IEEE Access, vol. 6, pp. 51931–51938, 2018.
Ü. Önen and A. Çakan, “Anti-swing control of an operhead crane by using genetic algorithm based LQR,” International Journal of Engineering and Computer Science, vol. 6, no. 6, pp. 21612–21616, 2017.
D. G. V. da Fonseca, A. F. O. de A. Dantas, C. E. T. Dórea, and A. L. Maitelli, “Explicit GPC control applied to an approximated linearized crane system,” Journal of Control Science and Engineering, vol. 2019, pp. 1–13, 2019.
N. S. A. Shukor and M. A. Ahmad, “Data-driven PID tuning based on safe experimentation dynamics for control of double-pendulum-type overhead crane,” Intelligent Manufacturing and Mechatronics Proceedings of Symposium, Pekan, Pahang, Malaysia, pp. 295–303, 2018.
K. Nagarajan, “A predictive hill climbing algorithm for real valued multi-variable optimization problem like PID tuning,” International Journal of Machine Learning and Computing, vol. 8, no. 1, pp. 14–19, 2018.
V. H. A. Ribeiro and G. Reynoso-Meza, “Multi-objective PID controller tuning for an industrial gasifier,” Proc. of IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, pp. 1–6, 2018.
P. Mercadera, C. D. Canovas, and A. Banos, “Control PID multivariable de una caldera de vapor,” Revista Iberoamericana de Automática e Informática Industrial, vol. 16, pp. 15–25, 2019.
W. Qiao, X. Tang, and S. Zheng, “Adaptive two-degree-of-freedom PI for speed control of permanent magnet synchronous motor based on fractional order GPC,” ISA Transactions, vol. 64, pp. 303–313, 2016.
S. Lu, F. Zhou, and Y. Ma, “Predictive IP controller for robust position control of linear servo system,” ISA Transactions, vol. 63, pp. 211–217, 2016.
R. M. Miller, S. L. Shah, R. K. Wood, and E. K. Kwok, “Predictive PID,” ISA Transaction, vol. 38, no. 1, pp. 11–23, 1999.
T. Sato and A. Inoue, “Improvement of tracking performance in self-tuning PID controller based on generalized predictive control,” International Journal of Innovative Computing, Information and Control, vol. 2, no. 3, pp. 491–503, 2006.
T. Sato, “Design of GPC-based PID controller for controlling a weigh feeder,” Control Engineering Practice, vol. 18, no. 2, pp. 105–113, 2010.
X. Li, Y. Fang, and R. Zhang, “Hierarchical GPC-based PID control strategy for SST of USC und variable loads,” International Journal of Control and Automation, vol. 8, no. 10, pp. 1–14, 2015.
T. Sato, T. Yamamoto, N. Araki, and Y. Konishi, “Performance adaptive generalized predictive control based proportional-integral-derivative control system and its application,” Journal of Dynamic System, Measurement, and Control, vol. 136, no. 6, pp. 1–9, 2014.
D. W. Clark, C. Mohtadi, and P. S. Tuffs, “Generalized predictive control-Part I and II,” Automatica, pp. 137–160, 1995.
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Recommended by Associate Editor Yang Tang under the direction of Editor Hamid Reza Karimi. This work is supported by the National Natural Science Foundation of China (Grant no. 61174107) and the National Key R&D Program of China (Grant no. 2017YFC0805100).
Bin Yang received his master’s degree in control theory and control engineering from Wuhan University of science and technology, China, in 2007. He is studying for a Ph.D. in control science and engineering at Wuhan University of science and technology, China, and he is a associate professor at the Engineering and Technical College of Chengdu University of Technology, China. His research interests include intelligent control, system identification based on deep learning, and plasma configuration control in tokamak experiments.
Zhen-Xing Liu received his doctorate from Huazhong University of science and technology, China, in 2004. He is a professor and doctoral supervisor of School of information science and engineering, Wuhan University of science and technology, China. His research interests include advanced control theory and its application, new electric drive, and equipment fault diagnosis.
Hui-Kang Liu received his master’s degree in control theory and control engineering from Wuhan University of science and technology, China, in 1988. He is a professor and doctoral supervisor of School of information science and engineering, Wuhan University of science and technology, China. His research interests include intelligent equipment, new electric drive, equipment fault diagnosis.
Yan Li received her M.A. degree in English Literature and Linguistics from Beijing Foreign Language Studies University, China, in 2006 and has been promoted to the rank of associate professor since 2016. Her research interests include corpus-based translation, English literature and foreign language acquisition.
Sen Lin received his master’s degree in control theory and control engineering from Wuhan University of science and technology, China, in 2007. Now, he is studying for a Ph.D. in control science and engineering at Wuhan University of science and technology, China. His research interests include intelligent control, new electric drive, equipment fault diagnosis.
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Yang, B., Liu, ZX., Liu, HK. et al. A GPC-based Multi-variable PID Control Algorithm and Its Application in Anti-swing Control and Accurate Positioning Control for Bridge Cranes. Int. J. Control Autom. Syst. 18, 2522–2533 (2020). https://doi.org/10.1007/s12555-019-0400-2
- Anti-swing control
- bridge crane
- generalized predictive control
- multivariable PID controller