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Smart Structures and Systems
  Volume 32, Number 6, December 2023 , pages 359-369
DOI: https://doi.org/10.12989/sss.2024.32.6.359
 


Servo control strategy for uni-axial shake tables using long short-term memory networks
Pei-Ching Chen and Kui-Xing Lai

 
Abstract
    Servo-motor driven uniaxial shake tables have been widely used for education and research purposes in earthquake engineering. These shake tables are mostly displacement-controlled by a digital proportional-integral-derivative (PID) controller; however, accurate reproduction of acceleration time histories is not guaranteed. In this study, a control strategy is proposed and verified for uniaxial shake tables driven by a servo-motor. This strategy incorporates a deep-learning algorithm named Long Short-Term Memory (LSTM) network into a displacement PID feedback controller. The LSTM controller is trained by using a large number of experimental data of a self-made servo-motor driven uniaxial shake table. After the training is completed, the LSTM controller is implemented for directly generating the command voltage for the servo motor to drive the shake table. Meanwhile, a displacement PID controller is tuned and implemented close to the LSTM controller to prevent the shake table from permanent drift. The control strategy is named the LSTM-PID control scheme. Experimental results demonstrate that the proposed LSTM-PID improves the acceleration tracking performance of the uniaxial shake table for both bare condition and loaded condition with a slender specimen.
 
Key Words
    acceleration tracking; deep learning; Long Short-Term Memory network; servo control; shake table
 
Address
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