JACIII Vol.25 No.6 pp. 963-973
doi: 10.20965/jaciii.2021.p0963


Soft Sensor Model for Estimating the POI Displacement Based on a Dynamic Neural Network

Yujie Li*,**, Ming Zhang*,**,†, Yu Zhu*,**, Xin Li*,**, and Leijie Wang*,**

*State Key Lab of Tribology, Tsinghua University
Haidian District, Beijing 100084, China

**Beijing Key Lab of Precision/Ultra-Precision Manufacturing Equipments and Control, Tsinghua University
Haidian District, Beijing 100084, China

Corresponding author

February 3, 2020
August 20, 2021
November 20, 2021
point of interest (POI), flexible deformation, dynamic neural network (DNN), soft sensor model, stepwise-weight method (SWM)

To satisfy the increasingly demanding requirements in throughput and accuracy, more lightweight structures and a higher control bandwidth are highly desirable in next-generation motion stages. However, these requirements lead to a more flexible deformation, causing the estimation accuracy of the point of interest (POI) displacement to be guaranteed under the rigid-body assumption. In this study, a soft sensor model is constructed using a dynamic neural network (DNN) to estimate the POI displacement. This model can reflect the dynamic characteristics of the POI and realize accurate estimations. Moreover, a method combining stepwise and weight methods is proposed to analyze the influence of different DNNs, and a performance measure is presented to evaluate the soft sensor model. In the simulation, the DNN with the hidden feedbacks is proved to be the most suitable soft sensor model. The relative error and correlation coefficient obtained were less than 2% and 0.9998, respectively, during training and 5% and 0.9989, respectively, during testing. Compared with the data-driven model using the least-squares method, the proposed method exhibits a higher precision, and the relative error is within the setting range using the proposed performance measure.

Deformation of the POIs

Deformation of the POIs

Cite this article as:
Y. Li, M. Zhang, Y. Zhu, X. Li, and L. Wang, “Soft Sensor Model for Estimating the POI Displacement Based on a Dynamic Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.25 No.6, pp. 963-973, 2021.
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