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Genetic algorithms based adaptive active vibration control of a flexible structure

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper investigates the development of an active vibration control (AVC) mechanism for a flexible plate structure using a genetic modelling strategy where the utilisation of genetic algorithms (GAs) for dynamic modelling of the system is considered. The global search technique of GAs is used to obtain a dynamic model of a flexible plate structure based on one-step-ahead (OSA) prediction and verified within the AVC system. The GA based AVC algorithm thus developed is implemented within a flexible plate simulation environment and its performance in the reduction of deflection at the centre of the plate is assessed. The validation of the algorithm is presented in both the time and frequency domains. An assessment of the results thus obtained is given in comparison to the AVC system using conventional recursive least squares (RLS) method. Investigations reveal that the developed GA based AVC system performs better in the suppression of vibration of a flexible plate structure compared to an RLS based AVC system.
Czasopismo
Rocznik
Strony
65--79
Opis fizyczny
Bibliogr.24 poz.,
Twórcy
  • Automatic Control Systems Engineering Department University of Sheffield, Sheffield, United Kingdom
autor
  • Automatic Control Systems Engineering Department University of Sheffield, Sheffield, United Kingdom
Bibliografia
  • [1] Tokhi M. O., Hossain M. A., Self-tuning active vibration control of flexible beam structures, Research report (No. 531), Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, U.K., 1994.
  • [2] Leitch R. R. Tokhi M. O., Active noise control systems, IEE Proceedings-A, 134, 1987, 525-546.
  • [3] Tokhi M. O., Leitch R. R., Design and implementation of self-tuning active noise control systems, IEE Proceedings-D, 138, (5), 1991, 421-430.
  • [4] Goldberg D. E., Smith R. E., Non-stationary function optimisation using genetic dominance and diploidy, Proceedings of the 2nd International Conference on Genetic Algorithms, 1987, 59-68.
  • [5] Katz A. J., Thrift P. R., Generating image filters for target recognition by genetic learning, The IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 1994, 906-910.
  • [6] Mackle G., Savic D. A., Walters G. A., Application of genetic algorithms to pump scheduling for water supply, The 1st IEE/IEEE International Conference on GA in Engineering Systems: Innovations and Applications, Sheffield, U.K., 1995, 400-405.
  • [7] Man K. F., Tang K. S., Kwong S., Genetic Algorithms: concepts and applications. The IEEE Transactions on Industrial Electronics, 43, (5), 1996, 519-533.
  • [8] Roth G., Levine M. D., Geometric primptive extraction using a genetic algorithm, The IEEE Transaction on Pattern Analysis and Machine Intelligence, 16, 1994, 901-905
  • [9] Wang P., Kowk D. P., Optimal design of PID process controllers based on genetic algorithms, Control Engineering Practice, 2, (4), 1994, 641-648.
  • [10] Mat Darus I. Z., Токні M. O., Modelling of a flexible plate structure using finite difference methods. Proceeding of the 2nd World Engineering Conference, Malaysia, 2002.
  • [11] Kirkpatrick S., Gerlatt C. D. Jr., Vecchi M. P., Optimization by Simulated Annealing, Science, 220, 1983,671-680.
  • [12] Darwin C., On The Origin of Species, 1st edition (facsimile - 1964), Harvard University Press, Cambridge, MA, 1959.
  • [13] Dawkins R., The Blind Watchmaker, Penguin, London 1986.
  • [14] Goldberg D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA, 1989.
  • [15] Holland J. H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor. MI, 1975.
  • [16] Goldberg D. E., Smith R. E., Non-stationary function optimisation using genetic dominance and diploidy, Proceedings of the 2nd International Conference on Genetic Algorithms, 1987, 59-68.
  • [17] Katz A. J., Thrift P. R., Generating image filters for target recognition by genetic learning, The IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 1994, 906-910.
  • [18] Mackle G., Savic D. A., Walters G. A., Application of genetic algorithms to pump scheduling for water supply, The 1st IEE/IEEE International Conference on GA in Engineering Systems: Innovations and Applications, Sheffield, U.K., 1995,400-405.
  • [19] Man K. F., Tang K. S., Kwong S., Genetic Algorithms: concepts and applications, The IEEE Transactions on Industrial Electronics, 43, (5), 1996, 519-533.
  • [20] Roth G., Levine M. D., Geometric primptive extraction using a genetic algorithm, The IEEE Transaction on Pattern Analysis and Machine Intelligence, 16, 1994, 901-905.
  • [21] Wang P., Kowk D. P., Optimal design of PID process controllers based on genetic algorithms, Control Engineering Practice, 2, (4), 1994, 641-648.
  • [22] Chipperfield A. J., Fleming P. J., Parallel Genetic Algorithms: A Survey, Research report No. 518, Department of Automatic Control and Systems Engineering, The University of Sheffield, U.K., 1994.
  • [23] Elanayar S. V. Т., Yung C. S., Radial basis function neural networks for approximation and estimation of non-linear stochastic dynamic systems, The IEEE Transaction on Neural Networks, 5, (4), 1994, 594-603.
  • [24] Kargupta H., Smith R. E., System identification with evolving polynomial networks, Proceeding of the 4th International Conferences on Genetic Algorithms, University of California, San Diego, U.S.A., 1991,370-376.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPW4-0002-0121
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