Neural network control for earthquake structural vibration reduction using MRD

doi:10.1007/s11709-019-0544-4

Front. Struct. Civ. Eng.

2019, Vol. 13

Issue (5) : 1171-1182 https://doi.org/10.1007/s11709-019-0544-4

RESEARCH ARTICLE

Neural network control for earthquake structural vibration reduction using MRD

Khaled ZIZOUNI¹(

), Leyla FALI², Younes SADEK², Ismail Khalil BOUSSERHANE^1,³

¹. ArcihPEL Laboratory, University TAHRI Mohammed, Bechar, PB 417, Algeria
². FIMAS Laboratory, University TAHRI Mohammed, Bechar, PB 417, Algeria
³. Laboratory of Smart-Grid and Renewable Energies, University TAHRI Mohammed, Bechar, PB 417, Algeria

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Abstract

Structural safety of building particularly that are intended for exposure to strong earthquake loads are designed and equipped with high technologies of control to ensure as possible as its protection against this brutal load. One of these technologies used in the protection of structures is the semi-active control using a Magneto Rheological Damper device. But this device need an adequate controller with a robust algorithm of current or tension adjustment to operate which is further discussed in the following of this paper. In this study, a neural network controller is proposed to control the MR damper to eliminate vibrations of 3-story scaled structure exposed to Tōhoku 2011 and Boumerdès 2003 earthquakes. The proposed controller is derived from a linear quadratic controller designed to control an MR damper installed in the first floor of the structure. Equipped with a feedback law the proposed control is coupled to a clipped optimal algorithm to adapt the current tension required to the MR damper adjustment. To evaluate the performance control of the proposed design controller, two numerical simulations of the controlled structure and uncontrolled structure are illustrated and compared.

Keywords MR damper semi-active control earthquake vibration neural network linear quadratic control

Corresponding Author(s): Khaled ZIZOUNI

Just Accepted Date: 24 May 2019 Online First Date: 08 July 2019 Issue Date: 11 September 2019

Cite this article:

Khaled ZIZOUNI,Leyla FALI,Younes SADEK, et al. Neural network control for earthquake structural vibration reduction using MRD[J]. Front. Struct. Civ. Eng., 2019, 13(5): 1171-1182.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-019-0544-4
https://academic.hep.com.cn/fsce/EN/Y2019/V13/I5/1171

Fig.1 Structural model of building with 3 DOF

Fig.2 Schematic of MR damper

Fig.3 The schematic diagram of structural vibration control using classical LQC

Fig.4 The proposed structure of neural network controller

Fig.5 Training of neural network to emulate the classical LQC controller

Fig.6 The structure of the proposed intelligent LQC based on NN for semi-active structural control

Fig.7 Time-scaled NS component of the ground acceleration for the 2011 Tōhoku earthquake

Fig.8 Time-scaled NS component of the ground acceleration for the 2003 Boumerdès earthquake

Fig.9 Displacement responses of the 1st floor under the 2011 Tōhoku earthquake

Fig.10 Displacement responses of the 2nd floor under the 2011 Tōhoku earthquake

Fig.11 Displacement responses of the 3rd floor under the 2011 Tōhoku earthquake

Fig.12 Force responses of MR damper under the 2011 Tōhoku earthquake

Fig.13 Learning strategy error under the 2011 Tōhoku earthquake

Fig.14 Displacement responses of the 1st floor under the 2003 Boumerdès earthquake

Fig.15 Displacement responses of the 2nd floor under the 2003 Boumerdès earthquake

Fig.16 Displacement responses of the 3rd floor under the 2003 Boumerdès earthquake

Fig.17 Force responses of MR damper under the 2003 Boumerdès earthquake

Fig.18 Learning strategy error under the 2003 Boumerdès earthquake

Tab.1 Maximum structural responses and peak reduction of the structure

1	T Cherkaoui, F Medina, D Hatzfeld. The Agadir earthquake of February 29, 1960, examination of some of the parameters. Monografia of Instituto Geografico Nacional, 1991, 8: 133–148
2	I G Buckle. Passive control of structures for seismic loads. In: Proceeding of the 12th World Conference on Earthquake Engineering. Auckland, 2000
3	C Riascos, J M Casas, P Thomson. Semi-active tuned liquid column damper implementation with real-time hybrid simulations. In: Proceeding SPIE, Active and Passive Smart Structures and Integrated Systems. Nevada, 2016, 1–9
4	N R Fisco, H Adeli. Smart structures: Part I-Active and semi-active control. Scientia Iranica, 2011, 18(3): 275–284 https://doi.org/10.1016/j.scient.2011.05.034
5	S J Dyke, B F Spencer, M K Sain, J D Carlson. Experimental verification of semi-active structural control strategies using acceleration feedback. In: Proceeding of the 3rd International Conference on Motion and Vibration Control. Chiba, 1996, 3: 291–296
6	J T P Yao. Concept of structural control. Journal of the Structural Division, 1972, 98(7): 1567–1574
7	S G Luca, F Chira, V O Rosca. Passive, active and semi-active control systems in civil engineering. Bulletin of the Polytechnic Institute of Jassy, Constructions. Architecture Section, 2005, 3–4: 23–31
8	D Cancellara, M Pasquino. A new passive seismic control device for protection of structures under anomalous seismic events. Applied Mechanics and Materials, 2011, 82: 651–656 https://doi.org/10.4028/www.scientific.net/AMM.82.651
9	G L C M Abreu, V Lopes Jr, M J Brennan. Robust control of a two-floors building model using active mass driver. In: Proceeding of ISMA2010 Including USD2010, 2012, 215–226
10	M Abdel-Rohman. Optimal design of active TMD for buildings control. Building and Environment, 1984, 19(3): 191–195 https://doi.org/10.1016/0360-1323(84)90026-X
11	M Battaini, F Casciati, L Faravelli. Fuzzy control of structural vibration: An active mass system driven by a fuzzy controller. Earthquake Engineering & Structural Dynamics, 1998, 27(11): 1267–1276 https://doi.org/10.1002/(SICI)1096-9845(1998110)27:11<1267::AID-EQE782>3.0.CO;2-D
12	S J Dyke, B F Spencer. A comparison of semi-active control strategies for the MR damper. In: Proceedings of the IASTED International Conference, Intelligent Information Systems. The Bahamas, 1997
13	J D Carlson. What makes a good MR fluid? Journal of Intelligent Material Systems and Structures, 2002, 13(7–8): 431–435 https://doi.org/10.1106/104538902028221
14	F Oliveira, M A Botto, P Morais, A Suleman. Semi-active structural vibration control of base-isolated buildings using magnetorheological dampers. Journal of Low Frequency Noise, Vibration and Active Control, 2017, 37(3): 565–576
15	O Yoshida, S J Dyke, L M Giacosa, K Z Truman. Experimental verification of torsional response control of asymmetric buildings using MR dampers. Earthquake Engineering & Structural Dynamics, 2003, 32(13): 2085–2105 https://doi.org/10.1002/eqe.316
16	F Casciati, J Rodellar, U Yildirim. Active and semi-active control of structures –– theory and applications: A review of recent advances. Journal of Intelligent Material Systems and Structures, 2012, 23(11): 1181–1195 https://doi.org/10.1177/1045389X12445029
17	K M Hamdia, M Silani, X Zhuang, P He, T Rabczuk. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, 206(2): 215–227 https://doi.org/10.1007/s10704-017-0210-6
18	K M Hamdia, T Lahmer, T Nguyen-Thoi, T Rabczuk. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304–313 https://doi.org/10.1016/j.commatsci.2015.02.045
19	T Pajchrowski, K Zawirski. Application of artificial neural network to robust speed control of servodrive. IEEE Transactions on Industrial Electronics, 2007, 54(1): 200–207 https://doi.org/10.1109/TIE.2006.888782
20	S Maiti, V Verma, C Chakraborty, Y Hori. An adaptive speed sensorless induction motor drive with artificial neural network for stability enhancement. IEEE Transactions on Industrial Informatics, 2012, 8(4): 757–766 https://doi.org/10.1109/TII.2012.2210229
21	K Zizouni, I K Bousserhane, A Hamouine, L M R Fali. Damper-LQR control for earthquake vibration mitigation. International Journal of Civil Engineering and Technology, 2017, 8(11): 201–207
22	X Liu, Y Wu, Y Zhang, S Xiao. A control method to make LQR robust: A planes cluster approaching mode. International Journal of Control, Automation, and Systems, 2014, 12(2): 302–308 https://doi.org/10.1007/s12555-012-0435-0
23	A Montazeri, J Poshtan, A Choobdar. Performance and robust stability trade-off in minimax LQG control of vibrations in flexible structures. Engineering Structures, 2009, 31(10): 2407–2413 https://doi.org/10.1016/j.engstruct.2009.05.011
24	V A Neelakantan, G N Washington. Vibration control of structural systems using MR dampers and a ‘Modified’ sliding mode control technique. Journal of Intelligent Material Systems and Structures, 2008, 19(2): 211–224 https://doi.org/10.1177/1045389X06074509
25	M Aldawod, B Samali, F Naghdy, K C S Kwok. Active control of along wind response of tall building using a fuzzy controller. Engineering Structures, 2001, 23(11): 1512–1522 https://doi.org/10.1016/S0141-0296(01)00037-2
26	A H Heidari, S Etedali, M R Javaheri-Tafti. A hybrid LQR-PID control design for seismic control of buildings equipped with ATMD. Frontiers of Structural and Civil Engineering, 2018, 12(1): 44–57 https://doi.org/10.1007/s11709-016-0382-6
27	K C Schurter, P N Roschke. Neuro-Fuzzy control of structures using magnetorheological dampers. In: Proceedings of the American control conference. Arlington, Virginia, 2001, 1097–1102
28	L M Jansen, S J Dyke. Semi-active control strategies for MR dampers: A comparative study. Journal of Engineering Mechanics, 2000, 126(8): 795–803 https://doi.org/10.1061/(ASCE)0733-9399(2000)126:8(795)
29	D A Pohoryles, P Duffour. Adaptive control of structures under dynamic excitation using magnetorheological dampers: An improved clipped-optimal control algorithm. Journal of Vibration and Control, 2015, 21(13): 2569–2582 https://doi.org/10.1177/1077546313510543
30	E C Bingham. An investigation of the laws of plastic flow. U.S. Bureau of Standards Bulletin, 1916, 13(2): 309–353 https://doi.org/10.6028/bulletin.304
31	D R Gamota, F E Filisko. Dynamic mechanical studies of electrorheological materials: Moderate frequencies. Journal of Rheology (New York, N.Y.), 1991, 35(3): 399–425 https://doi.org/10.1122/1.550221
32	M Ismail, F Ikhouane, J Rodellar. The hysteresis Bouc-Wen model, a survey. Archives of Computational Methods in Engineering, 2009, 16(2): 161–188 https://doi.org/10.1007/s11831-009-9031-8
33	B F Spencer Jr, S J Dyke, M K Sain, J D Carlson. Phenomenological model for Magneto-Rheological dampers. Journal of Engineering Mechanics, 1997, 123(3): 230–238 https://doi.org/10.1061/(ASCE)0733-9399(1997)123:3(230)
34	D S Naidu. Optimal Control Systems. Special Indian ed. FL: CRC Press, 2002
35	X Liu, Y Wu, Y Zhang, S Xiao. A control method to make LQR robust: A planes cluster approaching mode. International Journal of Control, Automation, and Systems, 2014, 12(2): 302–308 https://doi.org/10.1007/s12555-012-0435-0
36	S Barnett, C Storey. Some results on the sensitivity and synthesis of asymptotically stable linear and non-linear systems. Automatica, 1968, 4(4): 187–194 https://doi.org/10.1016/0005-1098(68)90013-7
37	P Lancaster, L Rodman. Algebraic Riccati Equations. Oxford: Oxford University Press, 1995
38	N Vu-Bac, T Lahmer, X Zhuang, T Nguyen-Thoi, T Rabczuk. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31 https://doi.org/10.1016/j.advengsoft.2016.06.005
39	X Cui, K G Shin. Direct control and coordination using neural networks. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 686–697 https://doi.org/10.1109/21.256542
40	A H Rabiee, A H D Markazi. Semi-active adaptive fuzzy sliding mode control of buildings under earthquake excitations. In: Proceedings of the 2nd World Congress on Civil, Structural, and Environmental Engineering. Barcelona, Spain, 2017
41	S J Dyke, B F Spencer Jr, M K Sain, J D Carlson. Modeling and control of magnetorheological dampers for seismic response reduction. Smart Materials and Structures, 1996, 5(5): 565–575 https://doi.org/10.1088/0964-1726/5/5/006

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