Comparison of Different Control Algorithms for a Gantry Crane System
Author(s)
Stefan Bruins*
ABSTRACT
For a gantry crane system, this paper presents a comparison between four control algorithms. These algo-rithms are being compared on simplicity, stability and robustness. Goal for the controller is to move the load on a gantry crane to a new position with minimal overshoot of the load and maximal speed of the load. An-other goal is to provide an insight in the behaviour of the possible controllers. In this article a parallel P-controller, cascade P-controller, fuzzy controller and an internal model controller are used. To be able to validate and design the controllers a model is derived from the gantry crane. The controllers and the model are being implemented in Matlab Simulink. Finally the controllers are validated and tuned in Labview on a laboratory gantry scrane scale model. Main conclusion is that all presented controllers can be used as a con-troller for the gantry crane system but the fuzzy controller is showing the best performance.
For a gantry crane system, this paper presents a comparison between four control algorithms. These algo-rithms are being compared on simplicity, stability and robustness. Goal for the controller is to move the load on a gantry crane to a new position with minimal overshoot of the load and maximal speed of the load. An-other goal is to provide an insight in the behaviour of the possible controllers. In this article a parallel P-controller, cascade P-controller, fuzzy controller and an internal model controller are used. To be able to validate and design the controllers a model is derived from the gantry crane. The controllers and the model are being implemented in Matlab Simulink. Finally the controllers are validated and tuned in Labview on a laboratory gantry scrane scale model. Main conclusion is that all presented controllers can be used as a con-troller for the gantry crane system but the fuzzy controller is showing the best performance.
KEYWORDS
Gantry Crane, Modelling, Control, Fuzzy, Internal Model Control, Control Algorithms, Scale Model, Labview, Matlab, Simulink
Gantry Crane, Modelling, Control, Fuzzy, Internal Model Control, Control Algorithms, Scale Model, Labview, Matlab, Simulink
Cite this paper
nullS. Bruins, "Comparison of Different Control Algorithms for a Gantry Crane System," Intelligent Control and Automation, Vol. 1 No. 2, 2010, pp. 68-81. doi: 10.4236/ica.2010.12008.
nullS. Bruins, "Comparison of Different Control Algorithms for a Gantry Crane System," Intelligent Control and Automation, Vol. 1 No. 2, 2010, pp. 68-81. doi: 10.4236/ica.2010.12008.
References
[1] S. J. Nordfjord and H. Pálsson, “LEGO-Crane Controller using RCX and RoboLab”. http://fuzzy.iau.dtu.dk/ download/lego9/lego9.pdf [2] M. A. Ahmad and A. N. K. Nasir, “Hybrid Input Shaping and LQR control schemes of a Gantry Crane System,” Proceedings of the 3rd International Conference on Mechatronics, ICOM’08, pp. 365-372 [3] S. W. Su, H. Nguyen, R. Jarman, J. Zhu, D. Lowe, P. McLean, S. Huang, N. T. Nguyen, R. Nicholson and K. Weng, “Model Predictive Control of Gantry Crane with Input Nonlinearity Compensation,” Proceedings of World Academy of Science, Engineering and Technology, Vol. 3, 8 february 2009, pp. 312-316. [4] H. Butler, G. Honderd and J. van Amerongen, “Model Reference Adaptive Control of a Gantry Crane Scale Model,” IEEE Control System, January 1991, pp. 57-62 [5] Wahyudi, J. Jalani, R. Muhida and M. J. E. Salami, “Control Strategy for Automatic Gantry Crane Systems: A Practical and Intelligent Approach,” International Journal of Advanced Robotic Systems, Vol. 4, No. 4, (2007), pp 447-456 [6] P. Reading, “One-Degree of Freedom Internal Model Control,” 2002, pp. 39-64. http://www.bgu.ac.il/chem_ eng/pages/Courses/oren%20courses/Chapter%203%20-%20corrected%2002.pdf
[1] S. J. Nordfjord and H. Pálsson, “LEGO-Crane Controller using RCX and RoboLab”. http://fuzzy.iau.dtu.dk/ download/lego9/lego9.pdf [2] M. A. Ahmad and A. N. K. Nasir, “Hybrid Input Shaping and LQR control schemes of a Gantry Crane System,” Proceedings of the 3rd International Conference on Mechatronics, ICOM’08, pp. 365-372 [3] S. W. Su, H. Nguyen, R. Jarman, J. Zhu, D. Lowe, P. McLean, S. Huang, N. T. Nguyen, R. Nicholson and K. Weng, “Model Predictive Control of Gantry Crane with Input Nonlinearity Compensation,” Proceedings of World Academy of Science, Engineering and Technology, Vol. 3, 8 february 2009, pp. 312-316. [4] H. Butler, G. Honderd and J. van Amerongen, “Model Reference Adaptive Control of a Gantry Crane Scale Model,” IEEE Control System, January 1991, pp. 57-62 [5] Wahyudi, J. Jalani, R. Muhida and M. J. E. Salami, “Control Strategy for Automatic Gantry Crane Systems: A Practical and Intelligent Approach,” International Journal of Advanced Robotic Systems, Vol. 4, No. 4, (2007), pp 447-456 [6] P. Reading, “One-Degree of Freedom Internal Model Control,” 2002, pp. 39-64. http://www.bgu.ac.il/chem_ eng/pages/Courses/oren%20courses/Chapter%203%20-%20corrected%2002.pdf