1. Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems
- Author
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Chen, Zhiyi, Maske, Harshal, Upadhyay, Devesh, Shui, Huanyi, Huan, Xun, and Ni, Jun
- Subjects
Electrical Engineering and Systems Science - Systems and Control ,Computer Science - Machine Learning - Abstract
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., Comment: The paper was in the proceeding of 2024 American Control Conference. This submitted version addresses a minor correction to one equation (Eq. 14), while the results and conclusions remain the same
- Published
- 2024