Deep Koopman-Based Control of Quality Variation in Multistage Manufacturing Systems

This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed to minimize the quality variations caused by process disturbances in MMSs. Notably, the control framework leverages a s...

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Bibliographic Details
Published in2024 American Control Conference (ACC) pp. 893 - 898
Main Authors Chen, Zhiyi, Maske, Harshal, Upadhyay, Devesh, Shui, Huanyi, Huan, Xun, Ni, Jun
Format Conference Proceeding
LanguageEnglish
Published AACC 10.07.2024
Subjects
Predictive models
Process control
Quality control
Stochastic processes
Training
Transforms
Uncertainty
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Summary:This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed to minimize the quality variations caused by process disturbances in MMSs. Notably, the control framework leverages a stochastic deep Koopman (SDK) model to capture the quality propagation mechanism in the MMSs, highlighted by its ability to transform the nonlinear propagation dynamics into a linear one. Two roll-to-roll case studies are presented to validate the proposed method and demonstrate its effectiveness. The overall method is suitable for nonlinear MMSs and does not require extensive expert knowledge.
ISSN:2378-5861
DOI:10.23919/ACC60939.2024.10644781