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Pole placement approach to discrete and neuro-fuzzy crane control system prototyping

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EN
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EN
Today are observed rising requirements regarding increase productivity, reduced labour and maintenance cost, as well as optimizing the effectiveness of the material handling. The overhead travelling cranes play important role in selected manufacture applications. The paper presents methods of crane dynamic modelling and anti-sway discrete crane control system determining with using pole placement method (PPM). The TSK neuro-fuzzy crane controller was shown in the paper, as well as method of adaptation its control parameters to various values of rope length and masses of the load variables. The results of experiments carried out on real object were presented as well. Presented in the paper methods of crane dynamic modelling and control algorithm determining allow to prototype the effective anti-sway crane control systems. The method of determining conventional anti-sway crane control system based on discrete controllers type of PD elaborated with using pole placement method (PPM) was described in the paper. The TSK neuro-fuzzy crane controller was shown in the paper as well as method of adaptation its control parameters to various values ofrope length l and masses of the load m variables. The results of experiments carried out with using adaptive neuro-fuzzy TSK controller shown robustness on changeability of these variables and effectiveness of proposed control system.
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  • AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Mickiewicza Ave. 30, 30-059 Kraków, Poland tel: +48 126173104, smoczek@agh.edu.pl
Bibliografia
  • [1] Benhidjeb, A., Gissinger, G. L., Fuzzy control of an overhead crane performance comparison with classic control, Control Engineering Practice, Vol. 3, No. 12, pp. 1687-1696, 1995.
  • [2] Giua, A., Seatzu, C., Usai, G., Observer-controller design for cranes via Lyapunov equivalence, Automatica, Vol. 35, No. 4, pp. 669-678, 1999.
  • [3] Hicar, M., Ritok, J., Robust crane contro, Acta Polytechnica Hungarica, Vol. 3, No. 2, pp. 91-101, 2006.
  • [4] Lew, J. Y., Halder, B., Experimental study of anti-swing crane control for a varying load, Proceedings of American Control Conference, Vol. 2, pp. 1434-1439, 2003.
  • [5] Mahfouf, M., Kee, C. H., Abbod, M. F., Linkens, D. A., Fuzzy logic-based anti-sway control design for overhead cranes, Neural Computating and Applications, Vol. 9, pp. 38-43, 2000.
  • [6] Mendez, J. A., Acosta, L., Moreno, L., Torres, S., Marichal, G. N., An application of a neural self controller to an overhead crane, Neural Computing and Applications, No. 8, pp. 143-150, 1999.
  • [7] Nalley, M., Trabia, M., Control of overhead crane using a fuzzy logic controller, Journal of Intelligent and Fuzzy Systems, Vol. 8, pp. 1-18, 2000.
  • [8] Smoczek, J., Szpytko, J., Human-Machine Interface implementation in designing crane control based on fuzzy logic algorithm, Proceeding of the 17th IFAC World Congress, pp. 15100-15105, Seoul, Korea 2008.
  • [9] Smoczek, J., Szpytko, J.: The HMI/SCADA in control systems and supervision processes of manufacturing transport, Journal of KONES: Powertain and Transport, Vol. 15, No. 3, pp. 499-507, 2008.
  • [10] Yi, J., Yubazaki, N., Hirota, K., Anti-swing fuzzy control of overhead travelling crane, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1298-1303, 2002.
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Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ5-0033-0087
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