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MOPSO based multi-objective robust H2/H∞ vibration control for typical engineering equipment

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Vibration control is critically important for engineering equipment, and in modern industrial engineering active strategies with robust performance are often adopted. In traditional studies, a single-objective consideration is often taken into account when robust control is performed, while a simultaneous multi-criterial consideration is ignored. The study outlined in this paper focuses on typical equipment, namely machinery and sensitive equipment. Meanwhile, evaluation of robust performances based on feedback control is considered as the vibration control objective, and performance indexes using H∞ and H2 criterion are regarded as fitness functions. In addition, the latest intelligent algorithm – MOPSO (multi-objective particle swarm optimization) is used and the SPEA2 (strength Pareto evolutionary algorithm 2) is also introduced for comparison as a representative of evolution algorithm. Numerical results show that the Pareto frontier of MOPSO is much smoother and more uniformly distributed than SPEA2, and even more important is that MOPSO can obtain a unique, global and optimal solution gbest, which can avoid having to select just one from a group of equivalent solutions Finally, an analysis of factors which affect the norms is performed, and the numerical verification shows that the disturbance type (single input or multi input) can apparently affect the magnitude of norms, and this finding can provide a broader understanding of robust vibration control. This research proposes a novel multi-objective optimization strategy for robust vibration control, while the traditional approaches can and are still employed. In addition, advanced artificial intelligence plays an important role in vibration detection in engineering application.
Rocznik
Strony
341--359
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
autor
  • Hefei University of Technology Tunxi Road 193, Hefei, Anhui Province, China
autor
  • China National Machinery Industry Corporation Danning Street 3, Haidian, Beijing, China
autor
  • Anhui Provincial Laboratory of Civil Engineering and Materials Tunxi Road 193, Hefei, Anhui Province, China
autor
  • North Road 160 of Xisihuan Street, Haidian, Beijing, China
autor
  • Zhongguancun North Street, Haidian, Beijing, China
Bibliografia
  • 1. Harris C.M., Shock and vibration handbook, 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, Proceedings of SPIE 1994 International Symposium on Optics, Imaging, and Instrumentation, International Society for Optics and Photonics, 38–49, 1994.
  • 3. Bronowicki A.J., MacDonald R., Gursel Y., Dual stage passive vibration isolation for optical interferometer missions, Astronomical Telescopes and Instrumentation, International Society for Optics and Photonics, 753–763, 2003.
  • 4. Daley S., Hat¨ onen J., Owens D.H. ¨ , Active vibration isolation in a “smart spring” mount using a repetitive control approach, Control Engineering Practice, 14, 9, 991–997, 2006.
  • 5. Karnopp D., Crosby M.J., Harwood R.A., Vibration control using semi-active force generators, Journal of Manufacturing Science and Engineering, 96, 2, 619–626, 1974.
  • 6. Hrovat D., Applications of optimal control to advanced automotive suspension design, Journal of Dynamic Systems, Measurement, and Control, 115, 2, 328–342, 1993.
  • 7. Du H., Yim Sze K., Lam J., Semi-active H∞ control of vehicle suspension with magnetorheological dampers, Journal of Sound and Vibration, 283, 3, 981–996, 2005.
  • 8. Baeyens E., Khargonekar P.P., Some examples in mixed H2/H∞ control, American Control Conference, Vol. 2, 1608–1612, 1994.
  • 9. Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multiobjective genetic algorithm: NSGA-II, Evolutionary Computation, IEEE Transactions on Evolutionary Computation, 6, 2, 182–197, 2002.
  • 10. Zitzler E., Thiele L., Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, Evolutionary Computation, 3, 4, 257–271, 1999.
  • 11. Laumanns M., SPEA2: Improving the strength Pareto evolutionary algorithm, 19–26, 2001.
  • 12. Molina-Cristóbal A., Griffin I.A., Fleming P.J., Linear matrix inequalities and evolutionary optimization in multiobjective control, International Journal of Systems Science, 37, 8, 513–522, 2006.
  • 13. Pedersen G.K.M., Langballe A.S., Wisniewski R., Synthesizing multi-objective H2/H∞ dynamic controller using evolutionary algorithms, 15th Triennial World Congress, 2002.
  • 14. Eberhart R.C., Kennedy J., A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39–42, 1995.
  • 15. 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.
  • 16. Coello C.A., Lechuga M.S., MOPSO: A proposal for multiple objective particle swarm optimization, Proceedings of the 2002 Congress on IEEE, 1051–1056, 2002.
  • 17. Fonseca C.M., Fleming P.J., An overview of evolutionary algorithms in multi-objective optimization, evolutionary computation, 3, 1, 1–16, 1995.
  • 18. 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, 41–49, 1987.
  • 19. Xu Y.L., Yang Z.C., Chen J., Liu H.J., Micro vibration control platform for high technology facilities subject to traffic-induced ground Motion, Engineering Structures, 25, 8, 1069-1082, 2003.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d6cb1447-368a-47bc-a7d2-cc605160ac51
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