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Learning-based Koopman Modeling for Efficient State Estimation and Control ...
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Learning-based Koopman Modeling for Efficient State Estimation and Control of Nonlinear Process by Xunyuan Yin Oct 4, 2024 Abstract: Industries are increasingly prioritizing heightened process operation safety, production consistency, efficiency, waste and emissions reduction, and profitability optimization. This new dynamic environment calls for smarter, more efficient, and more flexible integrated automation solutions that comprise efficient and easy-to-use monitoring, control, and beyond.... Considering these challenges, we have made attempts to address optimization-based control and state estimation for nonlinear processes within an alternative framework of Koopman modeling, which aims to construct data-driven linear dynamic models to predict the dynamical behavior of nonlinear processes. This presentation covers the following aspects:... Additionally, we plan to present our recent findings on integrating machine learning with the data-enabled predictive control (DeePC) framework. Our goals are twofold: a) to facilitate economic operation within the DeePC framework, and b) to reduce or eliminate the need for optimization during online control implementation. Bio: Xunyuan Yin ... is an Assistant Professor in the School of Chemistry, Chemical Engineering and Biotechnology at Nanyang Technological University (NTU), Singapore. His research interests include machine learning-based process modeling and control, distributed estimation and control, and process monitoring, and their applications to wastewater treatment, carbon capture processes, and a few other large-scale industrial systems and processes. He is an Associate Editor for Control Engineering Practice, and Digital Chemical Engineering. He is a member of the IEEE Control Systems Society Conference Editorial Board, and is a member of Journal of Process Control Paper Prize Selection Committee.
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