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

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.

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

References in corpus (4)

• Auto-Encoding Variational Bayes
• Deep learning for universal linear embeddings of nonlinear dynamics
• Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems
• Stochastic Adversarial Koopman Model for Dynamical Systems