Learning Robust State Observers using Neural ODEs (longer version)

• URL: http://arxiv.org/abs/2212.00866v2
• Date: Wed, 17 May 2023 14:16:56 GMT
• Title: Learning Robust State Observers using Neural ODEs (longer version)
• Authors: Keyan Miao and Konstantinos Gatsis
• Abstract summary: We present a methodology for the design of state observers for nonlinear systems based on Neural ODEs, learning Luenberger-like observers and their nonlinear extension. 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.
• Score: 1.0094821910320064
• License: http://creativecommons.org/licenses/by/4.0/
• 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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