DOI QR코드

DOI QR Code

Prediction and control of buildings with sensor actuators of fuzzy EB algorithm

  • Chen, Tim (AI LAB, Faculty of Information Technology, Ton Duc Thang University) ;
  • Bird, Alex (National Physical Laboratory) ;
  • Muhammad, John Mazhar (Computer Simulation Research Laboratory, University of Oxford) ;
  • Cao, S. Bhaskara (National University of Sciences and Technology (NUST), School of Natural Sciences) ;
  • Melvilled, Charles (National University of Sciences and Technology (NUST), School of Natural Sciences) ;
  • Cheng, C.Y.J. (Department of Automatic Control, University of Southern Queensland)
  • Received : 2018.08.15
  • Accepted : 2019.07.15
  • Published : 2019.09.25

Abstract

Building prediction and control theory have been drawing the attention of many scientists over the past few years because design and control efficiency consumes the most financial and energy. In the literature, many methods have been proposed to achieve this goal by trying different control theorems, but all of these methods face some problems in correctly solving the problem. The Evolutionary Bat (EB) Algorithm is one of the recently introduced optimization methods and providing researchers to solve different types of optimization problems. This paper applies EB to the optimization of building control design. The optimized parameter is the input to the fuzzy controller, which gives the status response as an output, which in turn changes the state of the associated actuator. The novel control criterion for guarantee of the stability of the system is also derived for the demonstration in the analysis. This systematic and simplified controller design approach is the contribution for solving complex dynamic engineering system subjected to external disturbances. The experimental results show that the method achieves effective results in the design of closed-loop system. Therefore, by establishing the stability of the closed-loop system, the behavior of the closed-loop building system can be precisely predicted and stabilized.

Keywords

References

  1. Adeli, H. and Jiang, X.M. (2006), "Dynamic fuzzy wavelet neural network model for structural system identification", J. Struct. Eng., ASCE, 132(1), 102-111. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:1(102).
  2. Adeli, H. and Kim, H. (2004), "Wavelet-hybrid feedback-least mean square algorithm for robust control of structures", J. Struct. Eng., ASCE, 130(2), 128-137. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:1(128).
  3. Athans, M., Kapasouris, P., Kappos, E. and Spang, H.A. (1986), "Linear quadratic gaussian with Loop-Transfer-Recovery Methodology for the F-100 engine", J. Guid. Control Dyn., 9(1), 45-51. https://doi.org/10.2514/3.20065.
  4. Braae, M. and Rutherford, D.A. (1979), "Theoretical and linguistic aspects of the fuzzy logic controller", Automatica, 15(5), 553-577. https://doi.org/10.1016/0005-1098(79)90005-0.
  5. Buvana, D. and Jayashree, R. (2019), "ANFIS controller-based cascaded nonisolated bidirectional DC-DC converter", J. Circuit. Syst. Comput., 28(1), 1950001. https://doi.org/10.1142/S0218126619500014.
  6. Chang, S.S.L. and Zadeh, L.A. (1972), "On fuzzy mapping and control", Fuzzy Sets, Fuzzy Logic, Fuzzy Syst., Selected Papers by Lotfi A Zadeh, 180-184. https://doi.org/10.1142/9789814261302_0012.
  7. Connor, J.J. (2003), Introduction to Structural Motion Control. Prentice-Hall, Upper Saddle River, NJ.
  8. Doyle, J.C. and Stein, G. (1981), "Multivariable feedback design: concepts for classical/modern synthesis", IEEE Tran. Auto. Control, 26(1), 4-16. https://doi.org/10.1109/TAC.1981.1102555.
  9. Enns, D.F. (1984), "Model reduction with balanced realizations: An error bound and frequency weighted generalization", Proc. 23rd Conference on Decision and Control, Las Vegas.
  10. Gauthier, D.J., Sukow, D.W., Concannon, H.M. and Socolar, J.E.S. (1994), "Stabilizing unstable periodic orbits in a fast diode resonator using continuous time-delay autosynchronization", Phys. Rev. E., 50, 2343-2346. https://doi.org/10.1103/PhysRevE.50.2343.
  11. Jiang, X.M. and Adeli, H. (2005), "Dynamic wavelet neural network for nonlinear identification of highrise buildings", Comput. Aid. Civil Infrastr. Eng., 20(5), 316-330. https://doi.org/10.1111/j.1467-8667.2005.00399.x.
  12. Kalman, R.E. (1963), "New methods in Wiener filtering theory", Eds. J.L. Bogdanoff and F. Kozin, Proceedings of the First Symposium on Engineering Applications of Random Function Theory and Probability, John Wiley & Sons, New York
  13. Kapitaniak, T., Kocarev, L.J. and Chua, L.O. (1993), "Controlling chaos without feedback and control signals", Int. J. Bifurcat. Chaos, 3, 459-468. https://doi.org/10.1142/S0218127493000362.
  14. Kickert, W.J.M. and Mamdani, E.H. (1978), "Analysis of a fuzzy logic controller", Fuzzy Sets Syst., 1(1), 29-44. https://doi.org/10.1016/B978-1-4832-1450-4.50033-X.
  15. Kim, H. and Adeli, H. (2004), "Hybrid feedback-least mean square algorithm for structural control", J. Struct. Eng., ASCE, 130(2), 120-127. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:1(120).
  16. Lu, L.T., Chiang, W.L. and Tang, J.P. (1998), "LQG/LTR control methodology in active structure control", J. Eng. Mech., ASCE, 124(4), 446-454. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(446).
  17. Maciejowski, J.M. (1989), Multivariable Feedback Design, Addition-Wesley Publishing Co., Chapter 5, 222-264.
  18. Moore, B.C. (1981), "Principal component analysis in linear systems: controllability, observability, and model reduction", IEEE Tran. Auto. Control, 26, 17-31. https://doi.org/10.1109/TAC.1981.1102568.
  19. Mossaheb, S. (1983), "Application of a method of averaging to the study of dithers in nonlinear systems", Int. J. Control, 38, 557-576. https://doi.org/10.1080/00207178308933094.
  20. Stein, G. and Athans, M. (1987), "The LQG/LTR procedure for multivariable feedback control design", IEEE Tran. Auto. Control, 32(2), 105-114. https://doi.org/10.1109/TAC.1987.1104550.
  21. Steinberg, A.M. and Kadushin, I. (1973), "Stabilization of nonlinear systems with dither control", J. Math. Anal. Appl., 43, 273-284. https://doi.org/10.1016/0022-247X(73)90275-8.
  22. Takagi, T. and Sugeno, M. (1985), "Fuzzy identification of systems and its applications to modeling and control", IEEE Trans. Syst., Man, Cybern., 1, 116-132. https://doi.org/10.1109/TSMC.1985.6313399.
  23. Tsai, P.W., Pan, J.S., Liao, B.Y., Tsai, M.J. and Istanda, V. (2012), "Bat algorithm inspired algorithm for solving numerical optimization problems", Appl. Mech. Mater., 148, 134-137. https://doi.org/10.4028/www.scientific.net/AMM.148-149.134.
  24. Wang, H.O. and Abed, E.H. (1995), "Bifurcation control of a chaotic system", Automatica, 31, 1213-1226. https://doi.org/10.1016/0005-1098(94)00146-A.
  25. Wang, H.O. and Tanaka, K. (1996), "An LMI-based stable fuzzy control of nonlinear systems and its application to control of chaos", IEEE Int. Conf. on Fuzzy Systems, 1433-1438. https://doi.org/10.1109/FUZZY.1996.552386.
  26. Wang, H.O., Tanaka, K. and Griffin, M.F. (1996), "An approach to fuzzy control of nonlinear systems: stability and design issues", IEEE Tran. Fuzzy Syst., 4, 14-23. https://doi.org/10.1109/91.481841.
  27. Yang, J.N., Wu, J.C., Samali, B. and Agrawal, A.K. (1998), "A benchmark problem for response control of wind-excited tall buildings", Proc. 2nd world Conference on Structural Control, 2, John Wiley & Sons, New York.
  28. Zadeh, L.A. (1965), "Fuzzy sets", Inform. Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X.
  29. Zames, G. and Shneydor, N.A. (1976), "Dither in nonlinear systems", IEEE Tran. Automat. Cortrol, 21, 660-667. https://doi.org/10.1109/TAC.1976.1101357.
  30. Zames, G. and Shneydor, N.A. (1977), "Structural stabilization on quenching by dither in nonlinear systems", IEEE Tran. Automat. Contr., 22, 353-361. https://doi.org/10.1109/TAC.1977.1101504.

Cited by

  1. Using Evolving ANN-Based Algorithm Models for Accurate Meteorological Forecasting Applications in Vietnam vol.2020, 2019, https://doi.org/10.1155/2020/8179652
  2. LMI based criterion for reinforced concrete frame structures vol.9, pp.4, 2020, https://doi.org/10.12989/acc.2020.9.4.407
  3. System simulation and synchronization for optimal evolutionary design of nonlinear controlled systems vol.26, pp.6, 2019, https://doi.org/10.12989/sss.2020.26.6.797
  4. Modified algorithmic LMI design with applications in aerospace vehicles vol.8, pp.1, 2019, https://doi.org/10.12989/aas.2021.8.1.069
  5. Optimized AI controller for reinforced concrete frame structures under earthquake excitation vol.11, pp.1, 2021, https://doi.org/10.12989/acc.2021.11.1.001