Intelligent Failure-Proof Control System for Structural Vibration.

K. Yoshida, T. Ōba
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Abstract

With progress of technology in recent years, gigantism and complication such as high-rise buildings, nuclear reactors and so on have brought about new problems. Particularly, the safety and the reliability for damages in abnormal situations have become more important. Intelligent control systems which can judge whether the situation is normal or abnormal at real time and cope with these situations suitably are demanded. In this study, Cubic Neural Network (CNN) is adopted, which consists of the controllers possessing cubically some levels of information abstracting. In addition to the usual quantitative control, the qualitative control is used for the abnormal situations. And by selecting a suitable controller, CNN can cope with the abnormal situation. In order to confirm the effectiveness of this system, the structural vibration control problems with sensory failure and elasto-plastic response are dealt with. As a result of simulations, it was demonstrated that CNN can cope with unexpected abnormal situations which are not considered in learning.
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结构振动智能防故障控制系统。
近年来随着科技的进步,高层建筑、核反应堆等的巨大性和复杂性带来了新的问题。特别是在异常情况下损坏的安全性和可靠性变得越来越重要。智能控制系统需要能够实时判断情况是否正常或异常,并适当地处理这些情况。本研究采用三次神经网络(Cubic Neural Network, CNN),由具有三次信息抽象层次的控制器组成。除了通常的定量控制外,对异常情况还采用定性控制。通过选择合适的控制器,CNN可以应对异常情况。为了验证该系统的有效性,研究了具有感官失效和弹塑性响应的结构振动控制问题。仿真结果表明,CNN可以应对学习中没有考虑到的意外异常情况。
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