Linearization of Recurrent-Neural-Network- Based Models for Predictive Control of Nano-Positioning Systems Using Data-Driven Koopman Operators

Linearization of Recurrent-Neural-Network- Based Models for Predictive Control of Nano-Positioning Systems Using Data-Driven Koopman Operators

Recent studies have shown that the nonlinear dynamics of nano-positioning systems (e.g., piezo-electric actuators (PEAs)) can be accurately captured by recurrent neural networks (RNNs). One direct application of this technique is PEA system control for precision positioning: linearize the nonlinear...

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Bibliographic Details
Main Authors: Shengwen Xie, Juan Ren
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Recurrent neural network
predictive control
Koopman operator
linearization
Online Access:https://ieeexplore.ieee.org/document/9154669/

Internet

https://ieeexplore.ieee.org/document/9154669/

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