Intelligent technology-based control of motion and vibration using MR dampers |
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Authors: | Li Zhou Chih-Chen Chang B F Spencer |
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Institution: | 1. College of Aerospace Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China 2. Department of Civil Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 3. Department of Civil & Environmental Engineering, University of Illinois at Urbana-Champaign, 205 North Matthews Ave, Urbana, IL 61801, USA |
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Abstract: | Due to their intrinsically nonlinear characteristics, development of control strategies that are implementable and can fully
utilize the capabilities of semiactive control devices is an important and challenging task. In this study, two control strategies
are proposed for protecting buildings against dynamic hazards, such as severe earthquakes and strong winds, using one of the
most promising semiactive control devices, the magnetorheological (MR) damper. The first control strategy is implemented by
introducing an inverse neural network (NN) model of the MR damper. These NN models provide direct estimation of the voltage
that is required to produce a target control force calculated from some optimal control algorithms. The major objective of
this research is to provide an effective means for implementation of the MR damper with existing control algorithms. The second
control strategy involves the design of a fuzzy controller and an adaptation law. The control objective is to minimize the
difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper.
The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the
control strategy based on the combination of the fuzzy controller and the adaptation law doesn’t require a prior knowledge
of the combined building-damper system, this approach provides a robust control strategy that can be used to protect nonlinear
or uncertain structures subjected to random loads.
Supported by: Hong Kong Research Grant Council Competitive Earmarked Research Grant HKUST 6218 / 99E and by the National Science Foundation
under grant CMS 99-00234. |
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Keywords: | neural networks models fuzzy control adaptation law nonlinear structure MR dampers |
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