| SMA-压电半主动混合阻尼器减震控制分析 |
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| 引用本文： | 展猛,,王社良,赵云.SMA-压电半主动混合阻尼器减震控制分析[J].世界地震工程,2018,34(4):024-30. |
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| 作者姓名： | 展猛 王社良 赵云 |
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| 作者单位： | 1. 黄淮学院 建筑工程学院, 驻马店 463000;2. 西安建筑科技大学 土木工程学院, 西安 710055;3. 天津大学 建筑工程学院, 天津 300000 |
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| 摘 要： | 将形状记忆合金（Shape memory alloy，简称"SMA"）与压电摩擦阻尼器复合设计了一种半主动混合阻尼器，对SMA丝进行了材性试验，分析了循环圈数、加载速率和应变幅值对SMA丝力学性能的影响。基于试验数据，以速度方向和应变值作为神经元输入，建立了SMA的BP神经网络本构模型，并利用T-S模糊逻辑求解压电陶瓷驱动器输出电压，对1个2层的钢框架结构进行了无控、SMA被动控制和混合控制MATLAB仿真分析。结果表明:SMA的BP网络模型预测的应力误差大多集中在20 MPa以内，且误差较大点主要集中在加载的初始段和卸载的结束段等数值较小的点，BP神经网络能够较好地预测SMA丝的本构曲线。相比SMA被动控制，混合阻尼器可以更加有效地降低结构的动力反应。
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| 关 键 词： | SMA 压电陶瓷 混合阻尼器 神经网络 MATLAB仿真 |
Seismic control analysis of SMA-piezoelectric semi-active hybrid damper |
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| ZHAN Meng,' target="_blank" rel="external">,WANG Sheliang,ZHAO Yun.Seismic control analysis of SMA-piezoelectric semi-active hybrid damper[J].World Information On Earthquake Engineering,2018,34(4):024-30. |
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| Authors: | ZHAN Meng " target="_blank">' target="_blank" rel="external"> WANG Sheliang ZHAO Yun |
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| Institution: | 1. College of Architecture Engineering Huanghuai University, Zhumadian 463000, China;2. College of Civil Engineering Xi’an University of Architecture and Technology, Xi’an 710055, China;3. College of Architecture Engineering Tianjin University, Tianjin 300000, China |
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| Abstract: | A semi-active hybrid damper was designed by combining shape memory alloy (SMA) with piezoelectric friction damper. The material test was carried out on SMA wire, and the influence of cycle number, loading rate and strain amplitude on mechanical properties of the SMA wire was analyzed. On the basis of the test data, taking the speed direction and strain values as input of neurons, and BP neural network constitutive model of SMA was established. Using T-S fuzzy logic to solve output voltage of piezoelectric ceramic driver, a two-storied steel frame structure was simulated by MATLAB program with no control, SMA passive control and hybrid control. The results show that the stress errors predicted by BP network model of SMA are mostly within 20 Mpa, and points with larger errors are mainly concentrated in the points with smaller stress values such as the initial stage of loading and the end stage of unloading, and the BP neural network can better predict the constitutive curve of SMA wire. Compared with the SMA passive control, the hybrid damper can more effectively reduce the dynamic responses of the structure. |
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| Keywords: | SMA piezoelectric ceramic hybrid damper neural network MATLAB simulation |
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