| 基于磁流变阻尼器的结构振动优化控制 |
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| 引用本文： | 周丽,张志成. 基于磁流变阻尼器的结构振动优化控制[J]. 振动工程学报, 2003, 16(1): 109-113 |
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| 作者姓名： | 周丽 张志成 |
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| 作者单位： | 1. 南京航空航天大学航空宇航学院,南京,210016
2. 香港科技大学土木工程系,香港 |
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| 基金项目： | 南京航空航天大学青年科学基金资助项目 (编号 :S0 0 44 9) |
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| 摘 要： | 磁流变（MR）阻尼器利用MR流提供可控性是当今最新的半主动控制装置，工程应用前景十分广阔，但由于MR阻尼器所固有的高度非线性动特性，使得描述其阻尼力-输入电压关系的逆向动特性的数学模型很难得到，而逆向动特性模型对实现整个控制策略又是至关重要的。本项研究针对这一问题提出运用神经网络技术建立MR阻尼器的神经网络模型来模拟其逆向动特性，并设计与之相适应的控制系统，建立起基于MR阻尼器的结构振动控制的有效分析方法，通过数值仿真结果探讨所提出的结构控制策略的有效性。
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| 关 键 词： | 磁流变阻尼器 优化控制 半主动控制 结构振动控制 |
| 修稿时间： | 2001-12-30 |
Optimal Vibration Control Using MR Dampers |
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| Zhou Li College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics Nanjing,) Chang Chih Chen. Optimal Vibration Control Using MR Dampers[J]. Journal of Vibration Engineering, 2003, 16(1): 109-113 |
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| Authors: | Zhou Li College of Aerospace Engineering Nanjing University of Aeronautics Astronautics Nanjing ) Chang Chih Chen |
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| Affiliation: | Zhou Li College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics Nanjing,210016) Chang Chih Chen |
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| Abstract: | A Magnetorhrological (MR) damper that uses MR fluids to provide controllable characteristics is one of the most promising semiactive control devices for civil structural vibration control. However, due to their highly nonlinear dynamic behaviour, it is very difficult to obtain an inverse MR damping mathematical model that has an explicit relationship between the desired damping force and command signal (voltage). This force voltage relationship is required especially for structural vibration control design using MR dampers. In this study, we explore such a possibility via the neural network (NN) technique. Recurrent NN models are constructed to emulate the inverse dynamics properties of the MR damper. It is attemted to design a corresponding control system, and develop an effective dynamic analysis method for structures treated with MR dampers. Numerical simulations are also presented to illustrate the effectiveness of the proposed control strategy |
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| Keywords: | MR damper neural networks optimal control inverse dynamics |
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