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Active vibration control with multi-objective control output for typical engineering equipment

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In traditional active vibration control, a single-objective control output is often considered and constrained, but in fact some conflicting performance indexes are always emerging simultaneously and a one-sided method for pursuing only one excellent output is adopted, which may sacrifice other control characteristics. In this paper, a novel active vibration control with multi-objective control output was proposed for machinery equipment and sensitive equipment, and the latest artificial intelligence – multi-objective particle swarm optimization (MOPSO) was utilized, and the active controller was evaluated by the H∞ criterion, meanwhile an active control with a single-objective control output was also carried out for comparison. Numerical studies demonstrated that a pair of conflicting indexes could be balanced well in the proposed strategy, and thus only one blindly pursued control output was effectively overcome.
Rocznik
Strony
405--422
Opis fizyczny
Bibliogr. 16 poz. rys., tab., wykr.
Twórcy
autor
  • China National Machinery Industry Corporation Danning Street 3, Haidian District, Beijing, P. R. China
autor
  • China IPPR International Engineering Co., Ltd West 3rd Ring North Road 5, Haidian District, Beijing, P.R. China
autor
  • China IPPR International Engineering Co., Ltd West 3rd Ring North Road 5, Haidian District, Beijing, P.R. China
autor
  • China IPPR International Engineering Co., Ltd West 3rd Ring North Road 5, Haidian District, Beijing, P.R. China
Bibliografia
  • 1. Harris C.M., Shock and vibration handbook, pp. 33–50, McGraw-Hill, New York, 1987.
  • 2. Beard A.M., Schubert D.W., von Flotow A.H., Practical product implementation of an active/passive vibration isolation system, SPIE’s 1994 International Symposium on Optics, Imaging, and Instrumentation, International Society for Optics and Photonics, pp. 38–49, 1994.
  • 3. Gawronski W., Advanced Structural Dynamics and Active Control of Structures, Springer-Verlag, 2004.
  • 4. Preumont A., Vibration Control of Active Structures, 3rd ed., Springer, 2011.
  • 5. Spencer Jr. B.F., Nagarajaiah S., State of the Art of Structural Control, ASCE Journal of Structural Engineering, 129(7): 845–856, 2003.
  • 6. Blachowski B., Model based predictive control of guyed mast vibration, Journal of Theoretical and Applied Mechanics, 45: 405–423, 2007.
  • 7. Pnevmatikos N., Gantes C., Control strategy for mitigating the response of structures subjected to earthquake actions, Engineering Structures, 32(11): 3616–3628, 2010.
  • 8. Khot S.M., Yelve N.P., Tomar R., Desai S., Vittal S., Active vibration control of cantilever beam by using PID based output feedback controller, Journal of Vibration and Control, 18(3): 366–372, 2012.
  • 9. Huang W., Xu J., Zhu D.Y., Lu J.W., Lu K.L., Hu M.Y., MOPSO based multiobjective robust H2/H∞ vibration control for typical engineering equipment, Engineering Transactions, 63(3): 341–359, 2015.
  • 10. Symans M.D, Kelly S.W., Fuzzy logic control of bridge structures using intelligent semiactive seismic isolation systems, Earthquake Engineering and Structural Dynamics, 28(1): 37–60, 1999.
  • 11. Eberhart R.C., Kennedy J., A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–42, 1995.
  • 12. Farshidianfar A., Saghafi A., Kalami S.M., Saghafi I., Active vibration isolation of machinery and sensitive equipment using H∞ control criterion and particle swarm optimization method, Meccanica, 47(2): 437–453, 2012.
  • 13. Coello Coello C.A., Lechuga M.S., MOPSO: A proposal for multiple objective particle swarm optimization, Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp. 1051–1056, 2002, doi: 10.1109/CEC.2002.1004388.
  • 14. Shi Y., Eberhart R., A modified particle swarm optimizer, IEEE World Congress on Computational Intelligence, pp. 69–73, 1998.
  • 15. Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2): 182– 197, 2002.
  • 16. Goldberg D.E., Richardson J., Genetic algorithms with sharing for multimodal function optimization, Genetic algorithms and their applications: Proceedings of the Second International Conference on Genetic Algorithms, pp. 41–49, 1987.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a09a0fb5-15fb-4e92-be2a-d3b01eee5568
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