TR2019-147
Robust Nonlinear State Estimation for Thermal-Fluid Models Using Reduced-Order Models: The Case of the Boussinesq Equations
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- "Robust Nonlinear State Estimation for Thermal-Fluid Models Using Reduced-Order Models: The Case of the Boussinesq Equations", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC40024.2019.9029593, December 2019, pp. 2157-2162.BibTeX TR2019-147 PDF
- @inproceedings{Benosman2019dec,
- author = {Benosman, Mouhacine and Borggaard, Jeff},
- title = {Robust Nonlinear State Estimation for Thermal-Fluid Models Using Reduced-Order Models: The Case of the Boussinesq Equations},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2019,
- pages = {2157--2162},
- month = dec,
- doi = {10.1109/CDC40024.2019.9029593},
- url = {https://www.merl.com/publications/TR2019-147}
- }
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- "Robust Nonlinear State Estimation for Thermal-Fluid Models Using Reduced-Order Models: The Case of the Boussinesq Equations", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC40024.2019.9029593, December 2019, pp. 2157-2162.
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Abstract:
We present a method for designing robust, proper orthogonal decomposition (POD)-based, low-order observers for a class of spectral infinite-dimensional nonlinear systems. Robustness to bounded model uncertainties is incorporated using the Lyapunov reconstruction approach from robust control theory. Furthermore, the proposed methodology includes a data-driven learning algorithm that auto-tunes the observer gains to optimize the performance of the state estimation. A challenging numerical example using the 2D Boussinesq equations demonstrates the effectiveness of the proposed observer.
Related News & Events
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NEWS MERL researchers presented 8 papers at Conference on Decision and Control (CDC) Date: December 11, 2019 - December 13, 2019
Where: Nice, France
MERL Contacts: Mouhacine Benosman; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano
Research Areas: Control, Machine Learning, OptimizationBrief- At the Conference on Decision and Control, MERL presented 8 papers on subjects including estimation for thermal-fluid models and transportation networks, analysis of HVAC systems, extremum seeking for multi-agent systems, reinforcement learning for vehicle platoons, and learning with applications to autonomous vehicles.