PSO iterative learning control algorithm for shaking table based on feedforward compensation

被引：0

作者：

An X. ^{[1
,2
]}

Gao F. ^{[1
]}

Yang Q. ^{[1
]}

Yang X. ^{[1
]}

机构：

[1] CEA Key Lab of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration (CEA), Harbin

[2] Heilongjiang Provincial Higher Institution Key Lab of Measurement Control Technology and Instrument, Harbin University of Science and Technology, Harbin

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 01期

关键词：

Feedforward inverse control; Iterative learning; Particle swarm optimization (PSO); Shaking table;

D O I：

10.13465/j.cnki.jvs.2022.01.027

中图分类号：

学科分类号：

摘要：

Here, aiming at problems of low recurrent accuracy and more iterations in seismic wave signal reproduction process of electromagnetic shaking table, based on the establishment of correct shaking table model, a feedforward inverse model compensation method based on acceleration model was proposed to mainly improve low-frequency characteristics of electromagnetic shaking table. In addition, aiming at slow convergence speed of the iterative learning control algorithm in waveform reproduction of shaking table, a feedback aided PD iterative learning algorithm with forgetting factor was proposed, and the improved adaptive PSO algorithm was used to optimize the control law's parameters off-line, improve reproduction accuracy and reduce number of iterations. The test results showed that this method can effectively improve reproduction accuracy with a small number of iterations. © 2022, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：213 / 220

页数：7

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