基于T-S型模糊神经网络的空间结构GMM作动器主动控制

杨 涛1, 王社良1,代建波2

振动与冲击 ›› 2015, Vol. 34 ›› Issue (24) : 1-6.

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (24) : 1-6.
论文

基于T-S型模糊神经网络的空间结构GMM作动器主动控制

  • 杨 涛1, 王社良1 , 代建波2
作者信息 +

Active Control of Spatial Structure by GMM Actuator Based on T-S Type Fuzzy Neural Network

  • YANG Tao1, WANG Sheliang1, DAI Jianbo2
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文章历史 +

摘要

基于自主研发的超磁致伸缩材料(Giant Magnetostrictive Material-GMM)作动器的主动控制特性,应用T-S(Takagi-Sugeno)型模糊神经网络设计了主动控制系统,该系统以GMM作动器两端节点的相对速度和相对位移作为输入,计算输出控制电流。通过神经网络的自适应学习功能进行模糊划分及生成模糊规则,利用模糊系统的推理能力对空间结构模型进行基于地震响应的主动控制仿真,同时与标准型模糊神经网络系统进行仿真对比。结果表明,二者对空间结构模型的主动控制都能达到良好效果,基于T-S型模糊神经网络推理简单,其仿真速度远快于标准型。因此,采用T-S型模糊神经网络对空间结构进行主动控制更能满足工程应用需求。

Abstract

Based on independent research and development of Giant Magnetostrictive Material (GMM) active control actuator, we designed a Takagi-Sugeno (T-S) fuzzy neural network control system of a spatial structure, in which the input are the relative displacement and relative speed of two nodes at the end of the active-member, and which can calculate the output control current. Taking advantage of the adaptive neural network learning function for fuzzy division and generating fuzzy rules, the  spatial structure model could be actively controlled the simulation by using fuzzy reasoning capability under the action of seismic response, and meanwhile compared with the results produced by the simulation of standard fuzzy neural network model. The results demonstrated that both two models can achieve good control effects, but the simulation speed of the T-S fuzzy neural network is far faster than the standard model. Therefore, the T-S fuzzy neural network controller can better meet the needs of engineering applications requirements.

关键词

GMM作动器 / 模糊神经网络 / 主动控制 / 仿真 / 空间结构

Key words

GMM active control actuator / fuzzy neural network / active control / simulation / spatial structure

引用本文

导出引用
杨 涛1, 王社良1,代建波2. 基于T-S型模糊神经网络的空间结构GMM作动器主动控制[J]. 振动与冲击, 2015, 34(24): 1-6
YANG Tao1, WANG Sheliang1, DAI Jianbo2. Active Control of Spatial Structure by GMM Actuator Based on T-S Type Fuzzy Neural Network[J]. Journal of Vibration and Shock, 2015, 34(24): 1-6

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