Smart Structures and Systems

  • 제27권4호
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  • Pages.705-717
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  • 2021
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Performance-based optimization of LQR for active mass damper using symbiotic organisms search

  • Chen, Pei-Ching (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology) ;
  • Sugiarto, Bryan J. (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology) ;
  • Chien, Kai-Yi (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology)
  • 투고 : 2020.08.07
  • 심사 : 2020.12.31
  • 발행 : 2021.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

The linear-quadratic regulator (LQR) has been applied to structural vibration control for decades; however, selection of the weighting matrices of an LQR mostly depends on trial and error. In this study, a novel metaheuristic optimization method named as symbiotic organisms search (SOS) algorithm is applied to tuning LQR weighting matrices for active mass damper (AMD) control systems. A 10-story shear building with an active mass damper installed at the top is adopted as a benchmark for numerical simulation in order to realize the optimization performance considering three objective functions for mitigation of structural acceleration. Two common optimization methods including genetic algorithm (GA), and particle swarm optimization (PSO) are also applied to this benchmark for comparison purposes. Numerical simulation results indicate that SOS is superior to GA and PSO on searching the minimized solution of the three objective functions. Meanwhile, minimizing the square root of the sum of the squares of peak modal acceleration achieves the best control performance of structural acceleration among the three objective functions. In addition, force saturation is proposed and applied in the optimization process such that the control force level is close to the force capacity of AMD under specified earthquake intensity. Furthermore, the control performance of the optimized LQR is compared with that of the LQR designed by applying three common weighting selection methods when the 10-story building is subjected to various earthquake excitations. Simulation results demonstrate that the optimized LQR significantly outperforms the three LQRs on structural acceleration responses as expected and reduces story drift slightly better than the three LQRs. Finally, the performance-based optimized LQR is experimentally validated by conducting shake table testing in the laboratory. The experimental results and structural control performance are discussed and summarized thoroughly.

키워드

과제정보

The authors would like to express our sincere gratitude to the experimental facilities and expense supported by NCREE (06109A2500) as well as the financial support provided by the Taiwan Building Technology Center, National Taiwan University of Science and Technology from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.

참고문헌

  1. Amini, F., Hazaveh, N.K. and Rad, A.A. (2013), "Wavelet PSO-based LQR algorithm for optimal structural control using active tuned mass dampers", Comput.-Aided Civil Infrastruct. Eng., 28(7), 542-557. https://doi.org/10.1111/mice.12017
  2. Alavinasab, A., Moharrami, M. and Khajepour, A. (2006), "Active control of structures using energy-based LQR method", Comput.-Aided Civil Infrastruct. Eng., 21(8), 605-611. https://doi.org/10.1111/j.1467-8667.2006.00460.x
  3. Bani-Hani, K.A. (2007), "Vibration control of wind-induced response of tall buildings with an active tuned mass damper using neural networks", Struct. Control Health Monitor., 14(1), 83-108. https://doi.org/10.1002/stc.85
  4. Bhushan, R., Chatterjee, K. and Shankar, R. (2016), "Comparison between GA-based LQR and conventional LQR control method of DFIG wind energy system", Proceedings of the 3rd International Conference on Recent Advances in Information Technology (RAIT), March. Dhanbad, India.
  5. Bozer, A. and Ozsariyildiz, S.S. (2018), "Free parameter search of multiple tuned mass dampers by using artificial bee colony algorithm", Struct. Control Health Monitor., 25(2), e2066. https://doi.org/10.1002/stc.2066
  6. Braz-Cesar, M.T. and Barros, R. (2018), "Semi-active fuzzy basedcontrol system for vibration reduction of a SDOF structure under seismic excitation excitation", Smart Struct. Syst., Int. J., 21(4), 389-395. https://doi.org/10.12989/sss.2018.21.4.389
  7. Cao, H. and Li, C. (2012), "Design of active tuned mass damper based on robust control", IEEE International Conference on Computer Science and Automation Engineering (CSAE), pp. 760-764.
  8. Cao, H., Reinhorn, A.M. and Soong, T.T. (1998), "Design of active mass damper for a tall TV tower in Nanjing China", Eng. Struct., 20(3), 134-143. https://doi.org/10.1016/S0141-0296(97)00072-2
  9. Celik, E. and Durgut, R. (2018), "Performance enhancement of automatic voltage regulator by modified cost function and symbiotic organisms search algorithm", Eng. Sci. Technol, Int. J., 21(5), 1104-1111. https://doi.org/10.1016/j.jestch.2018.08.006
  10. Chang, C.C. and Yang, H.T.Y. (1995), "Control of buildings using active tuned mass damper", J. Eng. Mech., 121(3), 355-366. https://doi.org/10.1061/(ASCE)0733-9399(1995)121:3(355)
  11. Chen, Z. and Casciati, S. (2012), "An active mass damper system for structural control using real-time wireless sensors", Struct. Control Health Monitor., 19(8), 758-767. https://doi.org/10.1002/stc.1485
  12. Chen, C.J. and Yang, S.M. (2014), "Application neural network controller and active mass damper in structural vibration suppression", J. Intell. Fuzzy Syst., 27, 2835-2845. https://doi.org/10.3233/IFS-141245
  13. Chen, P.C., Ting, G.C. and Li, C.H. (2020), "A versatile small-scale structural laboratory for novel experimental earthquake engineering", Earthq. Struct., Int. J., 18(3), 337-348. https://doi.org/10.12989/eas.2020.18.3.337
  14. Cheng, M.Y. and Prayogo, D. (2014), "Symbiotic organisms search: A new metaheuristic optimization algorithm", Comput. Struct., 139, 98-112. https://doi.org/10.1016/j.compstruc.2014.03.007
  15. Duan, H. and Sun, C. (2013), "Pendulum-like oscillation controller for micro aerial vehicle with ducted fan based on LQR and PSO", Sci. China Technol. Sci., 56(2), 423-429. https://doi.org/10.1007/s11431-012-5065-5
  16. Dyke, S.J., Spencer, Jr. B.F., Quast, P., Kaspari, Jr. D.C. and Sain, M.K. (1996), "Implementation of an active mass driver using acceleration feedback control", Microcomput. Civil Eng., 11, 305-323. https://doi.org/10.1111/j.1467-8667.1996.tb00445.x
  17. Jacknoon, A. and Abido, M.A. (2017), "Ant colony based LQR and PID tuned parameters for controlling inverted pendulum", Proceedings of International Conference on Communication, Control, Computing and Electronics Engineering, January, Khartoum, Sudan.
  18. Jang, D.D., Jung, H.J. and Moon, Y.J. (2014), "Active mass damper system using time delay control algorithm for building structure with unknown dynamics", Smart Struct. Syst., Int. J., 13(2), 305-318. https://doi.org/10.12989/sss.2014.13.2.305
  19. Jansen, L.M. and Dyke, S.J. (2000), "Semiactive control strategies for MR dampers: comparative study", J. Eng. Mech., 126(8), 795-803. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:8(795)
  20. Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of ICNN'95 - International Conference on Neural Networks, November-Deccember, Perth, WA, Australia.
  21. Li, L., Wang, N. and Qin, H. (2019), "Adaptive model reference sliding mode control of structural nonlinear vibration", Shock Vib., Article ID 3612516. https://doi.org/10.1155/2019/3612516
  22. Lim, C.W. (2008), "Active vibration control of the linear structure with an active mass damper applying robust saturation controller", Mechatronics, 18(8), 391-399. https://doi.org/10.1016/j.mechatronics.2008.06.006
  23. Moghaddasie, B. and Jalaeefar, A. (2019), "Optimization of LQR method for the active control of seismically excited structures", Smart Struct. Syst., Int. J., 23(3), 243-261. https://doi.org/10.12989/sss.2019.23.3.243
  24. Pnevmatikos, N.G. and Gantes, C.J. (2011), "Influence of time delay and saturation capacity to the response of controlled structures under earthquake excitations excitations", Smart Struct. Syst., Int. J., 8(5), 449-470. https://doi.org/10.12989/sss.2011.8.5.449
  25. Saaed, T.E., Nikolakopoulos, G., Jonasson, J.E. and Hedlund, H. (2015), "A state-of-the-art review of structural control systems", J. Vib. Control, 21(5), 919-937. https://doi.org/10.1177/1077546313478294
  26. Tan, P., Dyke, S.J., Richardson, A. and Abdullah, M. (2005), "Integrated device placement and control design in civil structures using genetic algorithms", J. Struct. Eng., 131(10), 1489-1496. https://doi.org/10.1061/(ASCE)0733-9445(2005)131:10(1489)
  27. Tejani, G.G., Pholdee, N., Bureerat, S., Prayogo, D. and Gandomi, A.H. (2019), "Structural optimization using multiobjective modified adaptive symbiotic organisms search", Expert Syst. Applic., 125, 425-441. https://doi.org/10.1016/j.eswa.2019.01.068
  28. Wang, H., Liao, L., Wang, D., Wen, S. and Deng, M. (2014), "Improved artificial bee colony algorithm and its application in LQR controller optimization", Mathe. Problems Eng., Article ID: 695637. https://doi.org/10.1155/2014/695637
  29. Wongsathan, C. and Sirima, C. (2008), "Application of GA to design LQR controller for an inverted pendulum system", Proceedings of 2008 IEEE International Conference on Robotics and Biomimetics, February. Bangkok, Thailand.
  30. Xiong, X. and Wan, Z. (2010), "The simulation of double inverted pendulum control based on particle swarm optimization LQR algorithm", Proceedings of 2010 IEEE International Conference on Software Engineering and Service Sciences, July, Beijing, China.
  31. Xu, K. and Igusa, T. (1992), "Dynamic characteristics of multiple substructures with closely spaced frequencies", Earthq. Eng. Struct. Dyn., 21(12), 1059-1070. https://doi.org/10.1002/eqe.4290211203
  32. Yang, D.H., Shin, J.H., Lee, H.W., Kim, S.K. and Kwak, M.K. (2017), "Active vibration control of structure by active mass damper and multi-modal negative acceleration feedback control algorithm", J. Sound Vib., 392, 18-30. https://doi.org/10.1016/j.jsv.2016.12.036
  33. Zaeri, A.H., Poodeh, M.B. and Eshtehardiha, S. (2007), "Improvement of Cuk converter performance with optimum LQR controller based on genetic algorithm", Proceedings of 2007 International Conference on Intelligent and Advanced Systems, November, Kuala Lumpur, Malaysia.