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首页> 外文会议>Conference on Active and passive smart structures and integrated systems; 20090309-12; San Diego, CA(US) >Application of self-tuning Gaussian networks for control of civil structures equipped with magnetorheological dampers
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Application of self-tuning Gaussian networks for control of civil structures equipped with magnetorheological dampers

机译：自整定高斯网络在装备磁流变阻尼器的土木结构控制中的应用

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摘要

This paper proposes an adaptive neural network composed of Gaussian radial functions for mapping the behavior of civil structures controlled with magnetorheological dampers. The online adaptation takes into account the limited force output of the semi-active dampers using a sliding mode controller, as their reaction forces are state dependent. The structural response and the actual forces from the dampers are used to adapt the Gaussian network by tuning the radial function widths, centers, and weights. In order to accelerate convergence of the Gaussian radial function network during extraordinary external excitations, the learning rates are also adaptive. The proposed controller is simulated using three types of earthquakes: near-field, mid-field, and far-field. Results show that the neural controller is effective for controlling a structure equipped with a magnetorheological damper, as it achieves a performance similar to the passive-on strategy while requiring as low as half the voltage input.
机译：本文提出了一种由高斯径向函数组成的自适应神经网络，用于绘制由磁流变阻尼器控制的土木结构的行为。在线调整考虑了使用滑模控制器的半主动阻尼器有限的力输出，因为它们的反作用力取决于状态。通过调节径向函数的宽度，中心和权重，结构响应和来自阻尼器的实际力用于调整高斯网络。为了在异常外部激励期间加速高斯径向函数网络的收敛，学习率也是自适应的。拟议的控制器是使用三种类型的地震来模拟的：近场，中场和远场。结果表明，神经控制器可有效控制配备了磁流变阻尼器的结构，因为它实现了与被动开启策略相似的性能，而所需的输入电压却低至一半。

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