Volume 2, Issue 1 (9-2017)                   NMCE 2017, 2(1): 24-36 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Rezaiee-Pajand M, Baghban A. Controlling structures by inverse adaptive neuro fuzzy inference system and MR dampers. NMCE 2017; 2 (1) :24-36
URL: http://nmce.kntu.ac.ir/article-1-102-en.html
1- Professor, Civil Engineering Department, Ferdowsi University of Mashhad, Iran. , rezaiee@um.ac.ir.
2- PhD student., Civil Engineering Department, Ferdowsi University of Mashhad, Iran. Email:Baghban.k@gmail.com.
Abstract:   (1498 Views)
To control structures against wind and earthquake excitations, Adaptive Neuro Fuzzy Inference Systems and Neural Networks are combined in this study. The control scheme consists of an ANFIS inverse model of the structure to assess the control force. Considering existing ANFIS controllers, which require a second controller to generate training data, the authors’ approach does not need another controller. To generate control force, active and semi-active devices could be used. Since the active ANFIS inverse controller may not guarantee a satisfactory response due to different uncertainties associated with operating conditions and noisy training data, this paper uses MR dampers as semi-active devices to provide control forces. To overcome the difficulty of tuning command voltage of MR dampers, a neural network inverse model is developed. The effectiveness of the proposed strategy is verified and illustrated using simulated response of the 3-story full-scale nonlinear benchmark building excited by several earthquake records through computer simulation. Moreover, the recommended control algorithm is validated using the wind-excited 76-story benchmark building equipped with MR and TMD dampers. Comparing results with other controllers demonstrates that the proposed method can reduce displacement, drift and acceleration, significantly.
Full-Text [PDF 954 kb]   (924 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/01/16 | Revised: 2017/05/23 | Accepted: 2017/07/2 | ePublished ahead of print: 2017/07/16

1. [1] Al-Dawod, M., Samali, B., Kwok, K. C. S., Naghdy, F., "Fuzzy controller for seismically excited nonlinear buildings", Journal of Engineering Mechanics, vol. 130(4), 2004, p. 407-415. [DOI:10.1061/(ASCE)0733-9399(2004)130:4(407)]
2. [2] Bani-Hani, K. A., Mashal, A., Sheban, M. A., "Semi-active neuro-control for base-isolation system using magnetorheological (MR) dampers", Earthquake Engineering & Structural Dynamics, vol. 35, 2006, p. 1119-1144. [DOI:10.1002/eqe.574]
3. [3] Choi, K. M., Cho, S. W., Jung, H. J., Lee, I. W., "Semi-active fuzzy control for seismic response reduction using magnetorheological dampers", Earthquake Engng Struct. Dyn., vol. 33, 2004, p. 723-736. [DOI:10.1002/eqe.372]
4. [4] Dyke, S. J., Spencer, B. F., Sain, M. K., Carlson, J. D., "Modeling and control of magnetorheological dampers for seismic response reduction", Smart Materials and Structures, vol. 5, 1996, p. 565-575. [DOI:10.1088/0964-1726/5/5/006]
5. [5] Dyke, S. J., Yi, F., Carlson, J. D., "Application of magnetorheological dampers to seismically excited structures", Proc., Int. Modal Anal. Conf., Bethel, Conn, 1999.
6. [6] Gu, Z. Q., Oyadiji, S. O., "Application of MR damper in structural control using ANFIS method", Computers and Structures, vol. 86, 2008, p. 427-436. [DOI:10.1016/j.compstruc.2007.02.024]
7. [7] Jang, J. S. R., "ANFIS: Adaptive network-based fuzzy inference systems", IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, 1993, p. 665-685. [DOI:10.1109/21.256541]
8. [8] Jung, H. J., Lee, H. J., Yoon, W. H., Oh, J. W., Lee, I. W., "Semiactive neurocontrol for seismic response reduction using smart damping strategy", Journal of Computing in Civil Engineering, vol. 18(3), 2004, p. 277-280. [DOI:10.1061/(ASCE)0887-3801(2004)18:3(277)]
9. [9] Kadhim, H. H., "Self Learning of ANFIS Inverse Control using Iterative Learning Technique", International Journal of Computer Applications, vol. 21(8), 2011, p. 24-29. [DOI:10.5120/2532-3450]
10. [10] Karamodin, A., Irani, F., Baghban, A., "Effectiveness of a fuzzy controller on the damage index of nonlinear benchmark buildings", Scientia iranica A, vol. 19(1), 2012, p. 1-10. [DOI:10.1016/j.scient.2011.12.002]
11. [11] Kerboua, M., Benguediab, M., Megnounif, A., Benrahou, K. H., Kaoulala, F., "Semi active control of civil structures, analytical and numerical studies", Physics Procedia, vol. 55, 2014, p. 301-306. [DOI:10.1016/j.phpro.2014.07.044]
12. [12] Kumar, R., Singh, S. P., Chandrawat, H. N., "MIMO adaptive vibration control of smart structures with quickly varying parameters: Neural networks vs classical control approach", Journal of Sound and Vibration, vol. 307, 2007, p. 639-661. [DOI:10.1016/j.jsv.2007.06.028]
13. [13] Kusagur, A., Kodad, S. F., Ram, B. V. S., "Modeling, Design & Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Speed Control of Induction Motor", International Journal of Computer Applications, vol. 6(12), 2010, p. 29-44. [DOI:10.5120/1123-1472]
14. [14] K-Karamodin, A., H-Kazemi, H., Akbarzadeh-T, M. R., "Semi-active control of structures using neuropredictive algorithm for MR dampers", 14th World Conference on Earthquake Engineering, Beijing, China, 2008. [DOI:10.1002/stc.278]
15. [15] Lee, H. J., Yang, Y. G., Jung, H. J., Spencer, B. F., Lee, I. W., "Semi-active neurocontrol of a base isolated benchmark structure", Structural Control and Health Monitoring, vol. 13, 2006, p. 682-692. [DOI:10.1002/stc.105]
16. [16] Marinaki, M., Marinakis, Y., Stavroulakis, G. E., "Fuzzy control optimized by a multi-objective differential evolution algorithm for vibration suppression of smart structures", Computers & Structures, vol. 147, 2015, p. 126-137. [DOI:10.1016/j.compstruc.2014.09.018]
17. [17] Ohtori, Y., Christenson, R. E., Spencer, Jr. B. F., Dyke, S. J., "Benchmark control problems for seismically excited nonlinear buildings", Journal of Engineering Mechanics, vol. 130(4), 2004, p. 366-387. [DOI:10.1061/(ASCE)0733-9399(2004)130:4(366)]
18. [18] Ohtori, Y., Spencer, B.F. Jr., "A MATLAB-based tool for nonlinear structural analysis", In the proceeding of the 13th ASCE engineering mechanics division specialty conference, Johns Hopkins university, Baltimore, June13-16, 1999.
19. [19] Park, K-S., Ok, S-Y., "Modal-space reference-model-tracking fuzzy control of earthquake excited structures", Journal of Sound and Vibration, vol. 334, 2015, p. 136-150. [DOI:10.1016/j.jsv.2014.09.009]
20. [20] Pourzeynali, S., Lavasani, H. H., Modarayi, A. H., "Active control of high rise building structures using fuzzy logic and genetic Algorithms", Engineering Structures, vol. 29, 2007, p. 346-357. [DOI:10.1016/j.engstruct.2006.04.015]
21. [21] Rezaiee-Pajand, M., Akbarzadeh-T., M. R., Nikdel, A., "Direct adaptive neurocontrol of structures under earth vibration", Journal of Computing in Civil Engineering, vol. 23(5), 2009, p. 299-307. [DOI:10.1061/(ASCE)0887-3801(2009)23:5(299)]
22. [22] Schurter, K. C., Roschke, P. N., "Neuro-Fuzzy Control of Structures Using Magnetorheological Dampers", Proceedings of the American Control Conference, Arlington, 2001, p. 25-27. [DOI:10.1109/ACC.2001.945866]
23. [23] Xu, Y. L., Qu, W. L., Ko, J. M., "Seismic response control of frame structures using magnetorheological/ electrorheological dampers", Earthq. Eng. Struct. Dyn., vol. 29, 2000, p. 557-75. https://doi.org/10.1002/(SICI)1096-9845(200005)29:5<557::AID-EQE922>3.0.CO;2-X [DOI:10.1002/(SICI)1096-9845(200005)29:53.0.CO;2-X]
24. [24] Yang, J., Wu, J., Samali, B., Agrawal, A., "A benchmark problem for response control of wind-excited tall building", Web Site (http://www.eng.uci.edu/~jnyang/benchmark.htm), 1999.
25. [25] Yi, F., Dyke, S. J., Caicedo, J. M., Carlson J. D., "Experimental Verification of Multi-Input Seismic Control Strategies for Smart Dampers", Journal of Engineering Mechanics, vol. 127(11), 2001, p. 1152-1164. [DOI:10.1061/(ASCE)0733-9399(2001)127:11(1152)]
26. [26] Yi, F., Dyke, S. J., Caicedo, J. M., Carlson, J. D., "Seismic Response Control Using Smart Dampers", Proc., American Control Conference, San Diego, CA, 2009, p. 1022-1026.

Add your comments about this article : Your username or Email:

Send email to the article author