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Data-driven stabilization of nonlinear systems

Published on:

7 April 2023

Primary Category:

Systems and Control

Paper Authors:

Robin Strässer,

Julian Berberich,

Frank Allgöwer

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Key Details

Proposes data-driven control design for nonlinear systems

Uses Koopman operator to lift nonlinear dynamics

Derives finite-dimensional bilinear approximation

Accounts for truncation error via finite-gain characterization

Provides LMI-based design procedure for robust stability

AI generated summary

Data-driven stabilization of nonlinear systems

This paper proposes a method to design stabilizing controllers for nonlinear systems using only measured data. It lifts the nonlinear dynamics to an infinite-dimensional linear system via the Koopman operator. Then a finite-dimensional approximation is derived and the resulting truncation error is characterized. Using robust control arguments, the paper shows how to obtain an LMI-based procedure that guarantees local stability for the nonlinear system.

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