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Fuzzy logic and neural network approach to the indirect adaptive pole placement crane control system

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EN
Abstrakty
EN
The problem under consideration in the paper of automation transportation operation realized by material handling devices is focused on time and accuracy of an overhead travelling crane's shifting process. The presented anti-sway crane control system was solved in the paper using combination of an indirect adaptive pole placement (IAPP) control method, fuzzy logic and artificial neural network. The presented approach to crane control is based on assuming structure of crane dynamic linear model with varying parameters, and linear closed-loop discrete control system consisting of proportional-derivative controllers with gains adjusted to changes of model's parameters using pole placement method (PPM). The parameters of crane dynamic model are estimated on-line using recursive least squares (RLS) algorithm. The estimation process is speeded up by neuro-fuzzy estimator, created using Takagi-Sugeno-Kang (TSK) fuzzy inference system, which determines the initial parameters of crane model based on scheduling variables, rope length and mass of a load changing in stochastic way. The neuro-fuzzy estimator is created in off-line process of neural network learning using least mean squares (LMS) method, based on a set of parametric output error models of crane dynamic identified for fixed values of rope length and mass of a load. The TSK estimator is next on-line improved by RLS algorithm.
Twórcy
autor
autor
  • AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Mickiewicza Av. 30, 30-059 Kraków, Poland tel.:+48 12 6173104, +48 12 6173103, smoczek@agh.edu.pl
Bibliografia
  • [1] Acosta, L., Méndez, J. A, Torres, S, Moreno, L., Marichal, G. N., On the design and implementation of a neuromorphic self-tuning controller, Neural Processing Letters 9, pp. 229–242, 1999.
  • [2] Al.-Garni, A. Z., Moustafa, K. A. F., Nizami, J. S. S. A. K., Optimal control of overhead cranes, Control Engineering Practice , Vol. 3, No. 9, pp. 1277-1284, 1995.
  • [3] Auernig, J. W., Troger, H., Time optimal control of overhead cranes with hoisting of the load. Automatica, Vol. 23, No. 4, pp. 437-447, 1987.
  • [4] Bartolini, G., Pisano, A., Usai, E., Second-order sliding-mode control of container cranes. Automatica 38, pp. 1783-1790, 2002.
  • [5] Cho, S. K., Lee, H. H., A fuzzy-logic antiswing controller for three-dimensional overhead cranes, ISA Transactions 41, pp. 235-243, 2002.
  • [6] Corriga, G., Giua, A., Usai, G., An implicit gain-scheduling controller for cranes, IEEE Transactions on Control Systems Technology, 6 (1), pp. 15-20, 1998.
  • [7] Giua, A., Seatzu, C., Usai, G., Observer-controller design for cranes via Lyapunov equivalence, Automatica, Vol. 35, No. 4 , pp. 669-678, 1999.
  • [8] Hicar, M., Ritok, J., Robust crane control, Acta Polytechnica Hungarica, Vol. 3, No. 2, pp. 91-101, 2006.
  • [9] Ishide, T., Uchida, H., Miyakawa, S., Application of a fuzzy neural network in the automation of roof crane system, Proceedings of the 9th Fuzzy System Symposium, pp. 29-32, 1993.
  • [10] Kang, Z., Fujii, S., Zhou, C., Ogata, K., Adaptive control of a planar gantry crane by the switching of controllers, Transactions of Society of Instrument and Control Engineers, Vol. 35, No. 2, pp. 253-261, 1999.
  • [11] 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.
  • [12] 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, No. 9, pp. 38-43, 2000.
  • [13] Smoczek, J., Szpytko, J., Pole placement approach to discrete and neuro-fuzzy crane control system prototyping, Journal of KONES Powertrain and Transport, Vol. 16, No. 4, pp. 435-445, 2009.
  • [14] Szpytko, J., Integrated decision making supporting the exploitation and control of transport devices, Uczelniane Wydawnictwa Naukowo-Dydaktyczne AGH, Kraków 2004.
  • [15] Szpytko, J., Transport devices quality control process integration. Proceedings of the Int. Conf. on CAD/CAM Robotics and Factories of the Future, Vellore, pp. 859-867, India 2006.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ7-0016-0099
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