Structural Damage Control with Interval Type-2 Fuzzy Logic Controller

Document Type : Research Article

Authors

Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

In this study, with designing of an Interval Type-2 Fuzzy Logic Controller (IT2FLC), the ability of this system to control the uncertainties governing the structure has been investigated. One of the main shortcomings of fuzzy systems is to consider the uncertainties in the fuzzy rule base. IT2FLS, which is in fact a development of fuzzy systems, has the ability to handle this problem and reducing the uncertainties surrounding it. In order to evaluate the performance of the proposed controller, building with the Magneto-rheological (MR) dampers have been used as benchmark. The results of the analysis of the structures in the proposed controller, with the uncontrolled structure, the controlled structures equipped with the type-1 fuzzy controller (FLC Type-1), as well as the controlled structures under the Genetic algorithm-Fuzzy Logic Controller (GA-FLC), have been compared and analyzed. Numerical results showed that IT2FLC is more effective in reducing the uncertainties governing the structure compared to other controllers, and the structural response will be optimized in different loading conditions. Using the proposed controller will reduce damage in the structure by 5 to 15 percent more than other controllers. In addition, the use of IT2FLC has reduced the displacement and acceleration time history responses of the structure compared with FLC-Type-1. The proposed controller has been able to reduce the maximum response of the different floors of structure by 10 to 30 percent compared to other controllers. Dynamic analysis of IDA method shows that at different load levels, the performance of IT2FLC will be more optimal than FLC-Type-1.

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Main Subjects


[1]S. Dyke, B. Spencer Jr, M. Sain, J. Carlson, Experimental verification of semi-active structural control strategies using acceleration feedback, in: Proc. of the 3rd Intl. Conf. on Motion and Vibr. Control, 1996, pp. 291-296.
[2] B. Spencer Jr, S. Dyke, M. Sain, J. Carlson, Phenomenological model for magnetorheological dampers, Journal of engineering mechanics, 123(3) (1997) 230-238.
[3] S. Dyke, B. Spencer Jr, M. Sain, J. Carlson, Modeling and control of magnetorheological dampers for seismic response reduction, Smart materials and structures, 5(5) (1996) 565.
[4] J. Carlson, Magneto-rheological fluid dampers for semi-active seismic control, in: Proc. 3rd Int. Conf. on Motion and Vibration Control, 1996-9, 1996, pp. 35-40.
[5] B. Spencer, J.D. Carlson, M. Sain, G. Yang, On the current status of magnetorheological dampers: seismic protection of full-scale structures, in: American Control Conference, Proceedings of the 1997, IEEE, 1997, pp. 458-462.
[6] G. Yang, B. Spencer Jr, J. Carlson, M. Sain, Large-scale MR fluid dampers: modeling and dynamic performance considerations, Engineering structures, 24(3) (2002) 309-323.
[7] L.M. Jansen, S.J. Dyke, Semiactive control strategies for MR dampers: comparative study, Journal of Engineering Mechanics, 126(8) (2000) 795-803.
[8] A. Karamodin, Damage Control of Structures (PhD dissertation), (2008).
[9] A. Karamodin, F. Irani, A. Baghban, Effectiveness of a fuzzy controller on the damage index of nonlinear benchmark buildings, Scientia Iranica, 19(1) (2012) 1-10.
[10] L. A. Zadeh, Fuzzy sets, Inf. Control, 8(3) (1965) 338–353.
[11] N.N. Karnik, J.M. Mendel, Type-2 fuzzy logic systems: type-reduction, in: Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on, IEEE, 1998, pp. 2046-2051.
[12] N.N. Karnik, J.M. Mendel, Centroid of a type-2 fuzzy set, Information Sciences, 132(1-4) (2001) 195-220.
[13] J.M. Mendel, Uncertain rule-based fuzzy logic system: introduction and new directions, (2001(.
[14] N.N. Karnik, J.M. Mendel, Introduction to type-2 fuzzy logic systems, in: Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, IEEE, 1998, pp. 915-920.
[15] J.M. Mendel, R.B. John, Type-2 fuzzy sets made simple, IEEE Transactions on fuzzy systems, 10(2) (2002) 117-127
[16] J.M. Mendel, R.I. John, F. Liu, Interval type-2 fuzzy logic systems made simple, IEEE transactions on fuzzy systems, 14(6) (2006) 808-821.
[17] J.M. Mendel, Advances in type-2 fuzzy sets and systems, Information sciences, 177(1) (2007) 84-110.
[18] D.G. Schwartz, The case for an interval-based representation of linguistic truth, Fuzzy Sets and Systems, 17(2) (1985) 153-165.
[19] M. Ghaemi, M.-R. Akbarzadeh-T, M. Jalaeian-F, Adaptive interval type-2 fuzzy PI sliding mode control with optimization of membership functions using genetic algorithm, in: Computer and Knowledge Engineering (ICCKE), 2012 2nd International eConference on, IEEE, 2012, pp.123-128.
[20] M. Ghaemi, M.-R. Akbarzadeh-T, M. Jalaeian-F, Optimal design of adaptive interval type-2 fuzzy sliding mode control using genetic algorithm, in: Control, Instrumentation and Automation (ICCIA), 2011 2nd International Conference on, IEEE, 2011, pp. 626-631.
[21] M.-Y. Hsiao, T.-H.S. Li, J.-Z. Lee, C.-H. Chao, S.-H. Tsai, Design of interval type-2 fuzzy sliding-mode controller, Information Sciences, 178(6) (2008) 1696-1716.
[22] T.-C. Lin, H.-L. Liu, M.-J. Kuo, Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems, Engineering Applications of Artificial Intelligence, 22(3) (2009) 420-430.
[23] D.W.W.W. Tan, A simplified type-2 fuzzy logic controller for real-time control, ISA transactions, 45(4) (2006) 503-516.
[24] H. Shariatmadar, S. Golnargesi, M.R. Akbarzadeh Totonchi, Vibration control of buildings using ATMD against earthquake excitations through interval type-2 fuzzy logic controller, Asian Journal of Civil Engineering-Building And Housing, 15(3) (2014) 321-338.
[25] D. Vamvatsikos, Seismic Performance, Capacity and Reliability of Structures as Seen Through Incremental Dynamic Analysis (PhD dissertation), (2005).
[26] Y. Ohtori, R. Christenson, B. Spencer Jr, S. Dyke, Benchmark control problems for seismically excited nonlinear buildings, Journal of Engineering Mechanics, 130(4) (2004) 366-385.
[27] N.N. Karnik, J.M. Mendel, Q. Liang, Type-2 fuzzy logic systems, IEEE transactions on Fuzzy Systems, 7(6) (1999) 643-658.
[28] Q. Liang and J. M. Mendel, Interval type-2 fuzzy logic systems: theory and design, IEEE Trans. Fuzzy Syst., 8(5) (2000) 535-550.
[29] V.V. Bertero, Strength and deformation capacities of buildings under extreme environments, Structural engineering and structural mechanics, 53(1) (1977) 29-79.
[30] N. Luco, C.A. Cornell, Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions, Earthquake Spectra, 23(2) (2007) 357-392.
[31] A. Nassar, H. Krawlinker, Seismic demands for SDOF and MDOF systems. Rep 95, the John Blume Earthquake Engineering Center, in, Stanford University, 1991.
[32] I. Psycharis, D. Papastamatiou, A. Alexandris, Parametric investigation of the stability of classical columns under harmonic and earthquake excitations, Earthquake engineering & structural dynamics, 29(8) (2000) 1093-1109.
[33] S.S.F. Mehanny, G.G. Deierlein, Modeling and assessment of seismic performance of composite frames with reinforced concrete columns and steel beams, Stanford University CA, 1999.
[34] G. De Matteis, R. Landolfo, D. Dubina, A. Stratan, Influence of the structural typology on the seismic performance of steel framed buildings, (2000).
[35] D. Vamvatsikos, C. Cornell, Seismic performance, capacity and reliability of structures as seen through incremental dynamic analysis, John A. Blume Earthquake Engineering Center Rep. No. 151, in, Stanford University, CA, 2002.
[36] F.E.M. Agency, Recommended Seismic Design Criteria for New Steel Moment-frame Buildings: Fema 350, Fema, 2013.