[1]ZOU Shourui,WU Yinan,FANG Yongchun.Tracking control of piezoelectric actuator based on feedforward compensation of recurrent neural network[J].CAAI Transactions on Intelligent Systems,2021,16(3):567-574.[doi:10.11992/tis.202104010]
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Tracking control of piezoelectric actuator based on feedforward compensation of recurrent neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 567-574 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2021-05-05
Title:
Tracking control of piezoelectric actuator based on feedforward compensation of recurrent neural network
Author(s):
ZOU Shourui;  WU Yinan;  FANG Yongchun
College of Artificial Intelligence, Nankai University, Tianjing 300350, China
Keywords:
piezoelectric actuator; hysteresis; nonlinearity; recurrent neural network; inverse model; feedforward control; neuron; adaptive control
CLC:
TP273
DOI:
10.11992/tis.202104010
Abstract:
However, PEAs’ inherent hysteresis, combined with other dynamic properties, negatively influences their tracking performance. Because recurrent neural networks can accurately fit nonlinear systems with memory storage capabilities, a recurrent neural network is designed to model the hysteresis of PEAs. Then, an accurate inverse model of the relationship between the output displacement and the input voltage is obtained, through which feedforward compensation is performed on PEAs. Furthermore, because modeling errors and other disturbances affect PEA tracking accuracy, a single neuron adaptive proportional-integral-derivative controller is designed to accurately track the desired signal by tracking the PEAs. Finally, experimental results verify the proposed model’s accuracy and the tracking performance of the designed controller.
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