AI-Based Model Estimation for a Precision Positioning Stage Employing Multiple Control Switching

Wang, Fu-Cheng; Zhong, Bo-Xuan; Wen, Chi-Wei; Tsai, I-Haur; Yen, Jia-Yush

doi:10.3390/mi16121305

Open AccessArticle

AI-Based Model Estimation for a Precision Positioning Stage Employing Multiple Control Switching

by

Fu-Cheng Wang

^1,*

,

Bo-Xuan Zhong

¹,

Chi-Wei Wen

¹,

I-Haur Tsai

² and

Jia-Yush Yen

^1,3

¹

Department of Mechanical Engineering, National Taiwan University, Taipei 106319, Taiwan

²

Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 106344, Taiwan

³

Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan

^*

Author to whom correspondence should be addressed.

Micromachines 2025, 16(12), 1305; https://doi.org/10.3390/mi16121305

Submission received: 23 October 2025 / Revised: 13 November 2025 / Accepted: 18 November 2025 / Published: 21 November 2025

(This article belongs to the Topic Innovation, Communication and Engineering)

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Abstract

In this paper, we propose a real-time model estimation framework using artificial intelligence techniques and apply it to a piezoelectric transducer (PZT) stage equipped with multiple switching controllers. Conventional fixed controllers often fail to satisfy diverse performance requirements: some achieve smooth but slow responses, while others deliver fast yet oscillatory behavior. To address this limitation, we developed a multi-controller switching mechanism that can select optimal control sequences based on predicted system responses, thereby enhancing overall performance. However, the existing mechanism relies on a nominal plant and neglects variations during operation. To address this problem, we employ the eXtreme Gradient Boosting (XGBoost) algorithm to construct a real-time model estimator, which continuously updates the system model during response prediction, thereby improving prediction accuracy. The corresponding controllers are then adjusted according to the updated models and integrated into the switching mechanism to further enhance performance. Finally, we validate the proposed approach through simulations and experiments.

Keywords: model estimation; artificial intelligence; control switching; PZT; stage

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MDPI and ACS Style

Wang, F.-C.; Zhong, B.-X.; Wen, C.-W.; Tsai, I.-H.; Yen, J.-Y. AI-Based Model Estimation for a Precision Positioning Stage Employing Multiple Control Switching. Micromachines 2025, 16, 1305. https://doi.org/10.3390/mi16121305

AMA Style

Wang F-C, Zhong B-X, Wen C-W, Tsai I-H, Yen J-Y. AI-Based Model Estimation for a Precision Positioning Stage Employing Multiple Control Switching. Micromachines. 2025; 16(12):1305. https://doi.org/10.3390/mi16121305

Chicago/Turabian Style

Wang, Fu-Cheng, Bo-Xuan Zhong, Chi-Wei Wen, I-Haur Tsai, and Jia-Yush Yen. 2025. "AI-Based Model Estimation for a Precision Positioning Stage Employing Multiple Control Switching" Micromachines 16, no. 12: 1305. https://doi.org/10.3390/mi16121305

APA Style

Wang, F.-C., Zhong, B.-X., Wen, C.-W., Tsai, I.-H., & Yen, J.-Y. (2025). AI-Based Model Estimation for a Precision Positioning Stage Employing Multiple Control Switching. Micromachines, 16(12), 1305. https://doi.org/10.3390/mi16121305

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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AI-Based Model Estimation for a Precision Positioning Stage Employing Multiple Control Switching

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