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Communication Dans Un Congrès Année : 2021

Deep KKL: Data-driven Output Prediction for Non-Linear Systems

Résumé

We address the problem of output prediction, ie. designing a model for autonomous nonlinear systems capable of forecasting their future observations. We first define a general framework bringing together the necessary properties for the development of such an output predictor. In particular, we look at this problem from two different viewpoints, control theory and data-driven techniques (machine learning), and try to formulate it in a consistent way, reducing the gap between the two fields. Building on this formulation and problem definition, we propose a predictor structure based on the Kazantzis-Kravaris/Luenberger (KKL) observer and we show that KKL fits well into our general framework. Finally, we propose a constructive solution for this predictor that solely relies on a small set of trajectories measured from the system. Our simulations show that our solution allows to obtain an efficient predictor over a subset of the observation space.
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Dates et versions

hal-03630581 , version 1 (05-04-2022)

Identifiants

Citer

Steeven Janny, Vincent Andrieu, Madiha Nadri, Christian Wolf. Deep KKL: Data-driven Output Prediction for Non-Linear Systems. 2021 60th IEEE Conference on Decision and Control (CDC), Dec 2021, Austin, France. pp.4376-4381, ⟨10.1109/CDC45484.2021.9683277⟩. ⟨hal-03630581⟩
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