Learning Robust State Observers using Neural ODEs (longer version)

Keyan Miao; Konstantinos Gatsis

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arXiv · 2212.00866

Learning Robust State Observers using Neural ODEs (longer version)

Keyan Miao·Konstantinos Gatsis

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Abstract

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

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BibTeX RIS

Keyan Miao, Konstantinos Gatsis. 2022-12-01. Learning Robust State Observers using Neural ODEs (longer version). https://arxiv.org/abs/2212.00866

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