Abstract
A novel neural associative memory-based structural control method, coined as AMOLCO, is proposed in this study. AMOLCO is an open-loop control system that autonomously and incrementally learns to suppress the structural vibration caused by dynamic loads such as wind excitations and earthquakes to stabilize high-rise buildings. First, AMOLCO incrementally learns the associative pair of input excitation from either winds or earthquakes and the corresponding output control response generated by standard optimal control only under a single simple condition (i.e., low wind conditions). After learning for a short period of time, i.e., 15 min, AMOLCO becomes capable of efficiently suppressing more intense structural vibrations such as those caused by very strong winds or even earthquakes. In this study, evaluation of the AMOLCO method is performed by using the physical simulation data. The results show that the control signal generated by AMOLCO is similar to that generated by the state-of-the-art control system used in a building. In addition, the resulting control signal is tested on a realistic simulation to affirm that the signal can control the structures. These results show that for the first time, AMOLCO offers another approach of structural control, which is inexpensive and stable similar to a standard open-loop system and also adaptive against disturbances and dynamic changes similar to a closed-loop system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kobori, T., et al.: Seismic response controlled structure with active mass driver system. Part 1: Design. Earthquake Eng. Struct. Dyn. 22, 133–139 (1991)
Spencer, B.F., Nagarajaiah, S.: State of the art of structural control. Jour. Struct. Eng. 129, 845–856 (2003)
Alkhatib, R., Golnaraghi, M.F.: Active structural vibration control: A review. The Shock and Vibration Gigest 35(5), 367–383 (2003)
Fujinami, T., et al.: A hybrid mass damper system controlled by H ∞  control theory for reducing bending-torsion vibration of an actual building. Earthquake Eng. Struct. Dyn. 30, 1639–1653 (2001)
Koike, Y., et al.: Application of V-shaped hybrid mass damper to high-rise buildings and verification of damper performance. In: Struct. Eng. World Conf., SEWC, T198-4 (1998)
Ikeda, Y.: Active and semi-active control of buildings in Japan. Jour. Japan Asso. Earthquake Eng. 4(3), 278–282 (2004)
Ribakov, Y., Reinhorn, A.M.: Design of amplified structural camping using optimal considerations. Jour. Struct. Eng. 129(10), 1422–1427 (2003)
Guclu, R., Yazici, H.: Vibration control of a structure with ATMD against earthquake using fuzzy logic controllers. Jour. Sound and Vibration 318(1-2), 36–49 (2008)
Yang, S.M., et al.: Structural vibration suppression by a neural-network controller with a mass-damper actuator. Jour. Vibration and Control 12(5), 495–508 (2006)
Chung, F.-L., Lee, T.: On fuzzy associative memory with multiple-rule storage capacity. IEEE Trans. Fuzzy Syst. 4(4), 375–384 (1996)
Sussner, P., Velle, E.: Implicative Fuzzy Associative Memories. IEEE Trans. Fuzzy Syst. 14(6), 793–807 (2006)
Sudo, A., et al.: Associative memory for online learning in noisy environments using self-organizing incremental neural network. IEEE Trans. Neural Netw. 20(6), 964–972 (2009)
Kosko, B.: Bidirectional associative memories. IEEE Trans. Syst. Man Cybern. SMC-18(1), 49–60 (1988)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. USA 79(8), 2554–2558 (1982)
Yamada, T., et al.: Sequential learning for associative memory using Kohenen feature map. In: Proc. Int. Joint Conf. Neural Netw. (1999)
Shen, F., Hasegawa, O.: An incremental network for on-line unsupervised classification and topology learning. Neural Netw. 19(1), 90–106 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kawewong, A., Koike, Y., Hasegawa, O., Sato, F. (2011). Fast and Incremental Neural Associative Memory Based Approach for Adaptive Open-Loop Structural Control in High-Rise Buildings. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-24965-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24964-8
Online ISBN: 978-3-642-24965-5
eBook Packages: Computer ScienceComputer Science (R0)