LSTM-Based Model Predictive Control of Piezoelectric Motion Stages for High-Speed Autofocus

In this article, we proposed a neural network-based model predictive control (MPC) of piezoelectric motion stages (PEMAs) for autofocus (AF). Rather than using an internal controller to account for the problematic hysteresis effects of the PEMA, we use the long short-term memory (LSTM) unit to integ...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2023-06, Vol.70 (6), p.6209-6218
Main Authors: Yan, Jingyang, DiMeo, Peter, Sun, Lu, Du, Xian
Format: Article
Language:English
Subjects:
Algorithms
Autofocus (AF)
Back propagation
Back propagation networks
Cameras
Cost function
Feedforward systems
Hysteresis
Jitter
Learning
Learning systems
model predictive control (MPC)
neural network modeling
Neural networks
piezo actuator
Piezoelectricity
Position measurement
Predictive control
Vibrations
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Summary:In this article, we proposed a neural network-based model predictive control (MPC) of piezoelectric motion stages (PEMAs) for autofocus (AF). Rather than using an internal controller to account for the problematic hysteresis effects of the PEMA, we use the long short-term memory (LSTM) unit to integrate the hysteresis effects and the focus measurement into a single learning-based model. Subsequently, a MPC method is developed based on this LSTM model that successfully finds the optimal focus position using a series of focus measurements derived from a sequence of images. To further improve the speed of the long short-term based MPC, an optimized backpropagation algorithm is proposed that optimizes the MPC cost function. Experiments verified our proposed method reduces at minimum 30% regarding AF time when compared to well-known ruled-based AF methods and other learning-based methods.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3192667