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• W3025508484 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." @default.
• W3025508484 created "2020-05-21" @default.
• W3025508484 creator A5053957972 @default.
• W3025508484 creator A5074220429 @default.
• W3025508484 date "2020-05-12" @default.
• W3025508484 modified "2023-09-27" @default.
• W3025508484 title "Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization" @default.
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• W3025508484 hasPublicationYear "2020" @default.
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