Neural network control for earthquake structural vibration reduction using MRD

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

References

[1] Cherkaoui T, Medina F, Hatzfeld D. The Agadir earthquake of February 29, 1960, examination of some of the parameters. Monografia of Instituto Geografico Nacional, 1991, 8: 133–148

[2] Buckle I G. Passive control of structures for seismic loads. In: Proceeding of the 12th World Conference on Earthquake Engineering. Auckland, 2000

[3] Riascos C, Casas J M, Thomson P. 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] Fisco N R, Adeli H. Smart structures: Part I-Active and semi-active control. Scientia Iranica, 2011, 18(3): 275–284

[5] Dyke S J, Spencer B F, Sain M K, Carlson J D. 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] Yao J T P. Concept of structural control. Journal of the Structural Division, 1972, 98(7): 1567–1574

[7] Luca S G, Chira F, Rosca V O. 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] Cancellara D, Pasquino M. A new passive seismic control device for protection of structures under anomalous seismic events. Applied Mechanics and Materials, 2011, 82: 651–656

[9] Abreu G L C M, Lopes V Jr, Brennan M J. Robust control of a two-floors building model using active mass driver. In: Proceeding of ISMA2010 Including USD2010, 2012, 215–226

[10] Abdel-Rohman M. Optimal design of active TMD for buildings control. Building and Environment, 1984, 19(3): 191–195

[11] Battaini M, Casciati F, Faravelli L. Fuzzy control of structural vibration: An active mass system driven by a fuzzy controller. Earthquake Engineering & Structural Dynamics, 1998, 27(11): 1267–1276

[12] Dyke S J, Spencer B F. 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] Carlson J D. What makes a good MR fluid? Journal of Intelligent Material Systems and Structures, 2002, 13(7–8): 431–435

[14] Oliveira F, Botto M A, Morais P, Suleman A. 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] Yoshida O, Dyke S J, Giacosa L M, Truman K Z. Experimental verification of torsional response control of asymmetric buildings using MR dampers. Earthquake Engineering & Structural Dynamics, 2003, 32(13): 2085–2105

[16] Casciati F, Rodellar J, Yildirim U. 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

[17] Hamdia K M, Silani M, Zhuang X, He P, Rabczuk T. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, 206(2): 215–227

[18] Hamdia K M, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304–313

[19] Pajchrowski T, Zawirski K. Application of artificial neural network to robust speed control of servodrive. IEEE Transactions on Industrial Electronics, 2007, 54(1): 200–207

[20] Maiti S, Verma V, Chakraborty C, Hori Y. An adaptive speed sensorless induction motor drive with artificial neural network for stability enhancement. IEEE Transactions on Industrial Informatics, 2012, 8(4): 757–766

[21] Zizouni K, Bousserhane I K, Hamouine A, Fali L M R. Damper-LQR control for earthquake vibration mitigation. International Journal of Civil Engineering and Technology, 2017, 8(11): 201–207

[22] Liu X, Wu Y, Zhang Y, Xiao S. A control method to make LQR robust: A planes cluster approaching mode. International Journal of Control, Automation, and Systems, 2014, 12(2): 302–308

[23] Montazeri A, Poshtan J, Choobdar A. Performance and robust stability trade-off in minimax LQG control of vibrations in flexible structures. Engineering Structures, 2009, 31(10): 2407–2413

[24] Neelakantan V A, Washington G N. 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

[25] Aldawod M, Samali B, Naghdy F, Kwok K C S. Active control of along wind response of tall building using a fuzzy controller. Engineering Structures, 2001, 23(11): 1512–1522

[26] Heidari A H, Etedali S, Javaheri-Tafti M R. 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

[27] Schurter K C, Roschke P N. Neuro-Fuzzy control of structures using magnetorheological dampers. In: Proceedings of the American control conference. Arlington, Virginia, 2001, 1097–1102

[28] Jansen L M, Dyke S J. Semi-active control strategies for MR dampers: A comparative study. Journal of Engineering Mechanics, 2000, 126(8): 795–803

[29] Pohoryles D A, Duffour P. 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

[30] Bingham E C. An investigation of the laws of plastic flow. U.S. Bureau of Standards Bulletin, 1916, 13(2): 309–353

[31] Gamota D R, Filisko F E. Dynamic mechanical studies of electrorheological materials: Moderate frequencies. Journal of Rheology (New York, N.Y.), 1991, 35(3): 399–425

[32] Ismail M, Ikhouane F, Rodellar J. The hysteresis Bouc-Wen model, a survey. Archives of Computational Methods in Engineering, 2009, 16(2): 161–188

[33] Spencer B F Jr, Dyke S J, Sain M K, Carlson J D. Phenomenological model for Magneto-Rheological dampers. Journal of Engineering Mechanics, 1997, 123(3): 230–238

[34] Naidu D S. Optimal Control Systems. Special Indian ed. FL: CRC Press, 2002

[35] Liu X, Wu Y, Zhang Y, Xiao S. A control method to make LQR robust: A planes cluster approaching mode. International Journal of Control, Automation, and Systems, 2014, 12(2): 302–308

[36] Barnett S, Storey C. Some results on the sensitivity and synthesis of asymptotically stable linear and non-linear systems. Automatica, 1968, 4(4): 187–194

[37] Lancaster P, Rodman L. Algebraic Riccati Equations. Oxford: Oxford University Press, 1995

[38] Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

[39] Cui X, Shin K G. Direct control and coordination using neural networks. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 686–697

[40] Rabiee A H, Markazi A H D. 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] Dyke S J, Spencer B F Jr, Sain M K, Carlson J D. Modeling and control of magnetorheological dampers for seismic response reduction. Smart Materials and Structures, 1996, 5(5): 565–575

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature