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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.

has been cited by the following article:

• TITLE: Comparison of Different Control Algorithms for a Gantry Crane System

  AUTHORS: Stefan Bruins

  KEYWORDS: Gantry Crane, Modelling, Control, Fuzzy, Internal Model Control, Control Algorithms, Scale Model, Labview, Matlab, Simulink

  JOURNAL NAME: Intelligent Control and Automation, Vol.1 No.2, November 26, 2010

  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.