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

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (1) : 213-220.

AN Xin1,2, GAO Feng1, YANG Qiaoyu1, YANG Xueshan1

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Abstract

In order to solve the problems of low precision and many iterations of electromagnetic shaking table in the process of seismic signal reproduction, on the basis of establishing an accurate shaking table model, a feedforward inverse model compensation method based on acceleration model is proposed to improve the low frequency characteristics of electromagnetic shaking table. In addition, in order to solve the problem that the iterative learning control algorithm has a large number of iterations in the waveform reproduction of the shaking table, an improved adaptive particle swarm optimization algorithm is proposed to optimize the parameters of the control law off-line to achieve the purpose of improving the recurrence accuracy and reducing the number of iterations. The experimental results show that this method can effectively improve the reproduction accuracy in the process of a small number of iterations.

Key words

Shaking table / feedforward inverse control, iterative learning, particle swarm optimization

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AN Xin1,2, GAO Feng1, YANG Qiaoyu1, YANG Xueshan1. PSO iterative learning control algorithm for shaking table based on feedforward compensation[J]. Journal of Vibration and Shock, 2022, 41(1): 213-220

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