Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study

• URL: http://arxiv.org/abs/2401.04508v1
• Date: Tue, 9 Jan 2024 11:54:54 GMT
• Title: Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
• Authors: Jan C. Schulze and Alexander Mitsos
• Abstract summary: We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.
• Score: 56.283944756315066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We use Koopman theory for data-driven model reduction of nonlinear dynamical systems with controls. We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation. We present a deep-learning approach to train the proposed models. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.

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