LSTM-Based Model Predictive Control of Piezoelectric Motion Stages for High-Speed Autofocus
LSTM-Based Model Predictive Control of Piezoelectric Motion Stages for High-Speed Autofocus
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DOI：
10.1109/tie.2022.3192667
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发表时间：
2023-06
影响因子：
7.7
通讯作者：
Jingyang Yan;Peter DiMeo;Lu Sun;Xian Du
中科院分区：
文献类型：
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作者：
Jingyang Yan;Peter DiMeo;Lu Sun;Xian Du
在本文中，我们提出了一种用于自动对焦（AF）的压电运动平台（PEMA）的基于神经网络的模型预测控制（MPC）。我们没有使用内部控制器来解决 PEMA 有问题的滞后效应，而是使用长短期记忆 (LSTM) 单元将滞后效应和焦点测量集成到单个基于学习的模型中。随后，基于该 LSTM 模型开发了 MPC 方法，该方法使用从图像序列导出的一系列焦点测量值成功找到最佳焦点位置。为了进一步提高基于长短期的MPC的速度，提出了一种优化的反向传播算法来优化MPC成本函数。实验证明，与众所周知的基于规则的 AF 方法和其他基于学习的方法相比，我们提出的方法至少减少了 30% 的 AF 时间。
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.