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Robust fuzzy model predictive control of an overhead crane

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EN
Abstrakty
EN
The method of controlling an overhead crane with respect to the variation of operating conditions and control constraints is developed using a model predictive control (MPC) and fuzzy interpolation applied in linear parameter varying (LPV) approach to crane dynamic modelling. The proposed control approach is based on the assumption that operating conditions vary within the known range of scheduling variables, and the parameters of a crane dynamic model can be interpolated by a quasi-linear fuzzy model designed through utilizing the P1-TS fuzzy theory. Hence, a crane dynamic is approximated through interpolation between a set of local linear models determined through identification experiments at the local operating points selected within the bounded intervals of scheduling variables. For the modelling assumptions, the control algorithm is developed based on a generalized predictive control (GPC) procedure taking into consideration the constraints on sway angle of a payload and control signal. Feasibility and applicability of the proposed control technique were confirmed during experiments carried out on a laboratory-scaled overhead crane. The results of experiments are presented and compared with performances of a fuzzy logic-based scheduling control scheme.
Twórcy
autor
  • AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Mickiewicza Avenue 30, 30-059 Krakow, Poland tel.: +48 12 6173104, +48 12 6173103AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Mickiewicza Avenue 30, 30-059 Krakow, Poland tel.: +48 12 6173104, +48 12 6173103
autor
  • AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Mickiewicza Avenue 30, 30-059 Krakow, Poland tel.: +48 12 6173104, +48 12 6173103
Bibliografia
  • [1] Arnold, E., Sawodny, O., Neupert, J., Schneider, K., Anti-sway system for boom cranes based on a model predictive control approach, Proceedings of IEEE International Conference on Mechatronics and Automation, pp. 1533-1538, Niagara Falls, Canada 2005.
  • [2] Clarke, D.W., Mohtadi, C., Tuffs, P. S., Generalized predictive control Part I. The basic algorithm, Automatica, Vol. 23 (2), pp. 137-148, 1987.
  • [3] Hyla, P., Szpytko, J., Vision method for rope angle swing measurement for overhead travelling crane – validation approach, Activities of Transport Telematics: Communications in Computer and Information Science, Vol. 395, pp. 370-377, 2013.
  • [4] Hyla, P., Szpytko, J., Crane payload position measurement vision-based system dedicated for anti-sway solutions, Telematics Support for Transport: Communications in Computer and Information Science, Vol. 471, pp. 404-413, 2014.
  • [5] Kalmari, J., Backman, J., Visala, A., Nonlinear model predictive control of hydraulic forestry crane with automatic sway damping, Computers and Electronics in Agriculture, Vol. 109, pp. 36-45, 2014.
  • [6] Kapernick, B., Graichen, K., Model predictive control of an overhead crane using constraint substitution, Proceedings of American Control Conference, pp. 3973-3978, Washington, DC, USA 2013.
  • [7] Kluska, J., Analytical methods in fuzzy modeling and control, Studies in Fuzziness and Soft Computing 241, Springer, Heidelberg 2009.
  • [8] Kłosiński, J., Swing-free stop control of the slewing motion of a mobile crane, Control Engineering Practice, Vol. 13, pp. 451-460, 2005.
  • [9] Kłosiński, J., Janusz, J., Nycz, R., The impact of the FLC controller’s setting on the precision of the positioning of a payload transferred by a mobile crane, Acta Mechanica et Automatica, Vol. 8, No. 4, pp. 181-184, 2014.
  • [10] Sioma, A., Tytko, A., Vision methods for assessing the geometrical parameters of steel ropes, Acta Mechanica et Automatica, Vol. 6, No. 1, pp. 63-67, 2012.
  • [11] Smalko, Z., Szpytko, J., Safety in engineering practice, Proceedings of 17th European Safety and Reliability Conference ESREL, pp. 1231-1237, Valencia, Spain 2009.
  • [12] Smoczek, J., P1-TS fuzzy scheduling control system design using local pole placement and interval analysis, Bulletin of the Polish Academy of Sciences Technical Sciences, Vol. 62, No. 3, pp. 455-464, 2014.
  • [13] Su, S. W., Nguyen, H., Jarman, R., Zhu, J., Lowe, D., McLean, P., Huang, S., Nguyen, N. T., Nicholson, R., Weng, K., Model predictive control of gantry crane with input nonlinearity compensation, World Academy of Science, Engineering and Technology, Vol. 3, No. 2, pp. 899-903, 2009.
  • [14] Sun, N., Fang, Y., Chen, H., A new antiswing control method for underactuated cranes with unmodeled uncertainties: theoretical design and hardware experiments, IEEE Transactions on Industrial Electronics, Vol. 62, No. 1, pp. 453-465, 2015.
  • [15] Tomczyk, J., Cink, J., Kosucki, A., Dynamics of an overhead crane under a wind disturbance condition, Automation in Construction, Vol. 42, pp. 100-111, 2014.
  • [16] Trąbka, A., The impact of the support system's kinematic structure on selected kinematic and dynamic quantities of an experimental crane, Acta Mechanica et Automatica, Vol. 8, No. 4, pp. 189-193, 2014.
  • [17] Vaughan, J., Yano, A., Singhose, W., Comparison of robust input shapers, Journal of Sound and Vibration, Vol. 315, pp. 797-815, 2015.
  • [18] Wu, Z., Xia, X., Optimal motion planning for overhead cranes, IET Control Theory and Applications, Vol. 8, No. 17, pp. 1833-1842, 2014.
  • [19] Zavari, K., Pipeleers, G., Swevers, J., Scheduled controller design: illustration on an overhead crane, IEEE Transactions on Industrial Electronics, Vol. 61, No. 7, pp. 3713-3718, 2014.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-6e705db3-e027-4c5f-903f-7a7adfdb2ca7
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