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Relying on recent research results on Neural ODEs, this paper presents amethodology for the design of state observers for nonlinear systems based onNeural ODEs, learning Luenberger-like observers and their nonlinear extension(Kazantzis-Kravaris-Luenberger (KKL) observers) for systems withpartially-known nonlinear dynamics and fully unknown nonlinear dynamics,respectively. In particular, for tuneable KKL observers, the relationshipbetween the design of the observer and its trade-off between convergence speedand robustness is analysed and used as a basis for improving the robustness ofthe learning-based observer in training. We illustrate the advantages of thisapproach in numerical simulations.

Learning Robust State Observers using Neural ODEs (longer version)