TR2021-101

Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization


    •  Chakrabarty, A., Benosman, M., "Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization", Automatica, 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,
      • url = {https://www.merl.com/publications/TR2021-101}
      • }
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  • Research Areas:

    Control, Machine Learning

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.

 

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  • Related Publication

  •  Chakrabarty, A., Benosman, M., "Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization", arXiv, May 2020.
    BibTeX arXiv
    • @article{Chakrabarty2020may,
    • author = {Chakrabarty, Ankush and Benosman, Mouhacine},
    • title = {Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization},
    • journal = {arXiv},
    • year = 2020,
    • month = may,
    • url = {https://arxiv.org/abs/2005.05888v1}
    • }