Closed-LSTM neural network based reference modification for trajectory tracking of piezoelectric actuator

Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change.

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“…These result can be compared with the ones obtained by the authors of Ref. [ 57 ], who obtained maximum modeling errors of 0.35

m for a NARX and 0.24 micro for a LSTM-NN network.…”

Section: Resultssupporting

confidence: 54%

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Uralde

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“…31,32 The LSTM structure includes three gates: the forget gate, the input gate, and the output gate, which work together to form the loop module of the network. 33,34 Each gate has a specic function:…”

Section: Deep Belief Networkmentioning

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Energy value measurement of milk powder using laser-induced breakdown spectroscopy (LIBS) combined with long short-term memory (LSTM)

Ding,

Zhao,

Shu

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“…Recognizing these challenges, recent decades have witnessed the integration of neural networks in hysteresis modeling of piezoelectric dynamic systems [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

An explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresis

Ni,

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Closed-LSTM neural network based reference modification for trajectory tracking of piezoelectric actuator

Cited by 10 publications

References 28 publications

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Energy value measurement of milk powder using laser-induced breakdown spectroscopy (LIBS) combined with long short-term memory (LSTM)

An explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresis

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