TR2021-101
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization
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- "Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization", Automatica, DOI: 10.1016/j.automatica.2021.109860, August 2021.BibTeX TR2021-101 PDF
- @article{Chakrabarty2021aug,
- author = {Chakrabarty, Ankush and Benosman, Mouhacine},
- title = {Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization},
- journal = {Automatica},
- year = 2021,
- month = aug,
- doi = {10.1016/j.automatica.2021.109860},
- url = {https://www.merl.com/publications/TR2021-101}
- }
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- "Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization", Automatica, DOI: 10.1016/j.automatica.2021.109860, August 2021.
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MERL Contacts:
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Research Areas:
Abstract:
Data generated from dynamical systems with unknown dynamics enable the learning of state observers that are: robust to modeling error, computationally tractable to design, and capable of operating with guaranteed performance. In this paper, a modular design methodology is formulated, that consists of three design phases: (i) an initial robust observer design that enables one to learn the dynamics without allowing the state estimation error to diverge (hence, safe); (ii) a learning phase wherein the unmodeled components are estimated using Bayesian optimization and Gaussian processes; and, (iii) a re-design phase that leverages the learned dynamics to improve convergence rate of the state estimation error. The potential of our proposed learning-based observer is demonstrated on a benchmark nonlinear system. Additionally, certificates of guaranteed estimation performance are provided.
Related News & Events
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NEWS Ankush Chakrabarty gave an invited talk at CRAN: Centre de Recherche en Automatique de Nancy, France Date: October 21, 2021
Where: Université de Lorraine, France
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Machine Learning, Multi-Physical Modeling, OptimizationBrief- Ankush Chakrabarty (RS, Multiphysical Systems Team) gave an invited talk on `Bayesian-Optimized Estimation and Control for Buildings and HVAC' at the Research Center for Automatic Control (CRAN) in the University of Lorraine in France. The talk presented recent MERL research on probabilistic machine learning for set-point optimization and calibration of digital twins for building energy systems.