TR2022-009

Co-Design of Reduced-Order Models and Observers from Thermo-Fluid Data


    •  Vijayshankar, S., Chakrabarty, A., Grover, P., Nabi, S., "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}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Dynamical Systems, Optimization

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.