Decentralized control for the seismic response of high-rise building structures based on GA-LSTM

被引：0
|
作者：
Gao J. [1 ]
Tu J. [1 ]
Liu K. [1 ]
Li Z. [1 ]
机构：
[1] Hubei Key Laboratory of Roadway Bridge and Structure Engineering, Wuhan University of Technology, Wuhan
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2021年 / 40卷 / 10期
关键词：
Decentralized control; Genetic algorithm (GA); Long short-term memory (LSTM) networks; Lyapunov stability theory; Structural vibration control;
D O I：
10.13465/j.cnki.jvs.2021.10.015
中图分类号：
学科分类号：
摘要：
High-rise buildings have complex structures and huge degrees of freedom. When active seismic control method is used, it is difficult to establish a precise structure model, and the overall control target is hard to achieve. Thus, an intelligent decentralized control method based on long short-term memory (LSTM) networks was proposed, based on the LSTM theory combined with the large-scale system decentralized control theory. Different decentralized controller types were constructed using the LSTM deep learning framework, and the sufficient conditions for the stability of decentralized controllers were derived according to the Lyapunov stability theory. A genetic algorithm (GA) was used to optimize the initial learning rate of the LSTM framework to improve the convergence speed and prediction accuracy of the decentralized controller. Taking a 20-layer benchmark model as a controlled object, the control performance of the GA-LSTM decentralized control method was studied and its effect was compared with the centralized control effect. The results show that the intelligent decentralized control method based on GA-LSTM simplifies the controller structure. Compared with the overall failure phenomena that may occur in the centralized control, it has higher reliability and better control effect. © 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：114 / 122
页数：8
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