Effect of selecting different membership functions on semi-active fuzzy control of adjacent buildings with MR damper

Document Type : Research Article

Authors

School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

The selection of appropriate membership functions for fuzzy control systems has always been a topic of discussion among researchers and has been generally determined by trial and error based on the experience of the control system designer. In this study, the control performances of type-1 and type-2 fuzzy systems with different membership functions in semi-active fuzzy control of two adjacent three- and nine-story buildings connected using MR damper under seismic excitations are discussed. In this study, two fuzzy systems have been used considering the type of membership function as well as the number of defined membership functions for each input. The examined membership functions are defined symmetrically and at the same intervals for comparison. The results of the control systems used for the type-1 and type-2 fuzzy algorithms are examined and compared to the uncontrolled mode by considering the triangular, Gaussian, and trapezoidal membership functions. The results obtained from the defined performance criteria show that in general, type-2 fuzzy systems perform better than type-1 fuzzy systems, due to the consideration of uncertainties and the use of membership functions intermittently. Fuzzy control systems with triangular membership functions have the best performance compared to other membership functions and the Gaussian membership function has shown close control performance to triangular function. Also, when more membership functions are used to determine the degree of membership of fuzzy language values, the fuzzy system becomes less sensitive to the type of membership function used.

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[1] S.N. Mandal, J.P. Choudhury, D. De, S.B. Chaudhuri, Role of membership functions in fuzzy logic for prediction of shoot length of mustard plant based on residual analysis, World academy of science, engineering and technology, 38 (2008) 378-384.
[2] A. Sadollah, Introductory Chapter: Which membership function is appropriate in fuzzy system?, in:  Fuzzy Logic Based in Optimization Methods and Control Systems and its Applications, 2018.
[3] G. Lambert-Torres, M.A. Carvalho, L.E.B. Da Silva, J.O. Pinto, Fitting fuzzy membership functions using genetic algorithms, in:  Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics.'cybernetics evolving to systems, humans, organizations, and their complex interactions'(cat. no. 0, IEEE, 2000, pp. 387-392.
[4] A. Esmin, A. Aoki, G. Lambert-Torres, Particle swarm optimization for fuzzy membership functions optimization, in:  IEEE International Conference on Systems, Man and Cybernetics, IEEE, 2002, pp. 6 pp. vol. 3.
[5] J. Zhao, B.K. Bose, Evaluation of membership functions for fuzzy logic controlled induction motor drive, in:  IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02, IEEE, 2002, pp. 229-234.
[6] J.G. Monicka, N.G. Sekhar, K.R. Kumar, Performance evaluation of membership functions on fuzzy logic controlled ac voltage controller for speed control of induction motor drive, International Journal of Computer Applications, 13(5) (2011) 8-12.
[7] D. Wu, Twelve considerations in choosing between Gaussian and trapezoidal membership functions in interval type-2 fuzzy logic controllers, in:  2012 IEEE International Conference on Fuzzy Systems, IEEE, 2012, pp. 1-8.
[8] P. Jouei, S. Pourzeynali, Optimization of fuzzy rules and shape of fuzzy logic membership functions in semi-active control of buildings using variable stiffness,  (2013).(in persian)
[9] O.A.M. Ali, A.Y. Ali, B.S. Sumait, Comparison between the effects of different types of membership functions on fuzzy logic controller performance, International Journal, 76 (2015) 76-83.
[10] S. Kim, M. Lee, J. Lee, A study of fuzzy membership functions for dependence decision-making in security robot system, Neural Computing and Applications, 28(1) (2017) 155-164.
[11] M. Babanezhad, A.T. Nakhjiri, A. Marjani, M. Rezakazemi, S. Shirazian, Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature, Scientific Reports, 10(1) (2020) 1-13.
[12] T. Sutikno, A.C. Subrata, A. Elkhateb, Evaluation of fuzzy membership function effects for maximum power point tracking technique of photovoltaic system, IEEE Access, 9 (2021) 109157-109165.
[13] R. Pelalak, A.T. Nakhjiri, A. Marjani, M. Rezakazemi, S. Shirazian, Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors, Scientific Reports, 11(1) (2021) 1-11.
[14] Y. Ohtori, R. Christenson, B. Spencer, S. Dyke, Benchmark control problems for seismically excited nonlinear buildings, Journal of engineering mechanics, 130(4) (2004) 366-385.
[15] O. Yoshida, S.J. Dyke, Seismic control of a nonlinear benchmark building using smart dampers, Journal of engineering mechanics, 130(4) (2004) 386-392.
[16] S.-Y. Ok, D.-S. Kim, K.-S. Park, H.-M. Koh, Semi-active fuzzy control of cable-stayed bridges using magneto-rheological dampers, Engineering structures, 29(5) (2007) 776-788.
[17] M. Abdeddaim, A. Ounis, N. Djedoui, M. Shrimali, Reduction of pounding between buildings using fuzzy controller,  (2016).