TR2022-009
Co-Design of Reduced-Order Models and Observers from Thermo-Fluid Data
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- "Co-Design of Reduced-Order Models and Observers from Thermo-Fluid Data", IFAC Journal of Systems and Control, DOI: 10.1016/j.ifacsc.2021.100181, Vol. 19, pp. 100181, January 2022.BibTeX TR2022-009 PDF
- @article{Vijayshankar2022jan,
- author = {Vijayshankar, Sanjana and Chakrabarty, Ankush and Grover, Piyush and Nabi, Saleh},
- title = {Co-Design of Reduced-Order Models and Observers from Thermo-Fluid Data},
- journal = {IFAC Journal of Systems and Control},
- year = 2022,
- volume = 19,
- pages = 100181,
- month = jan,
- doi = {10.1016/j.ifacsc.2021.100181},
- url = {https://www.merl.com/publications/TR2022-009}
- }
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- "Co-Design of Reduced-Order Models and Observers from Thermo-Fluid Data", IFAC Journal of Systems and Control, DOI: 10.1016/j.ifacsc.2021.100181, Vol. 19, pp. 100181, January 2022.
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Abstract:
This paper presents a method of co-design of models and observers for buoyancy-driven turbulent flows. Recent work on data-driven techniques for estimating turbulent flows typically involve obtaining a dynamical model using Dynamical Mode Decomposition (DMD) and using the model to design estimators. Unfortunately, such a sequential design could result in state-space models that do not possess control-theoretic properties (such as detectability) that ensure guaranteed performance of the observer. In this paper, we propose semi-definite programs (SDPs) that allow us to simultaneously construct observer gains, along with DMD models which exhibit desired properties. Since DMD models for turbulent flows are typically highdimensional, we provide a tractable algorithm for solving the high-dimensional SDP. We demonstrate the potential of our proposed approach on an industrial application using real-world data, and illustrate that the co-design significantly outperforms sequential design.