TR2022-063
Constrained Smoothers for State Estimation of Vapor Compression Cycles
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- "Constrained Smoothers for State Estimation of Vapor Compression Cycles", American Control Conference (ACC), DOI: 10.23919/ACC53348.2022.9867269, June 2022, pp. 2333-2340.BibTeX TR2022-063 PDF
- @inproceedings{Deshpande2022jun,
- author = {{Deshpande, Vedang and Laughman, Christopher R. and Ma, Yingbo and Rackauckas, Chris}},
- title = {Constrained Smoothers for State Estimation of Vapor Compression Cycles},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- pages = {2333--2340},
- month = jun,
- publisher = {IEEE},
- doi = {10.23919/ACC53348.2022.9867269},
- issn = {2378-5861},
- isbn = {978-1-6654-5196-3},
- url = {https://www.merl.com/publications/TR2022-063}
- }
,
- "Constrained Smoothers for State Estimation of Vapor Compression Cycles", American Control Conference (ACC), DOI: 10.23919/ACC53348.2022.9867269, June 2022, pp. 2333-2340.
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MERL Contacts:
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Research Areas:
Abstract:
State estimators can be a powerful tool in the development of advanced controls and performance monitoring capabilities for vapor compression cycles, but the nonlinear and numerically stiff aspects of these systems pose challenges for the practical implementation of estimators on large physics-based models. We develop smoothing methods in the extended and ensemble Kalman estimation frameworks that satisfy physical constraints and address practical limitations with standard implementations of these estimators. These methods are tested on a model built in the Julia language, and are demonstrated to successfully estimate unmeasured variables with high accuracy.
Related News & Events
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NEWS MERL researchers presented 9 papers at the American Control Conference (ACC) Date: June 8, 2022 - June 10, 2022
Where: Atlanta, GA
MERL Contacts: Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Abraham P. Vinod; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference in Atlanta, GA, MERL presented 9 papers on subjects including autonomous-vehicle decision making and motion planning, realtime Bayesian inference and learning, reference governors for hybrid systems, Bayesian optimization, and nonlinear control.