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Data-driven stabilization of nonlinear systems via descriptor embedding

Mohammad Alsalti, Claudio De Persis, Victor G. Lopez, Matthias A. Müller

发表年份
2025
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摘要

We introduce the notion of descriptor embedding for nonlinear systems and use it for the data-driven design of stabilizing controllers. Specifically, we provide sufficient data-dependent LMI conditions which, if feasible, return a stabilizing nonlinear controller of the form $u=K(x)Z(x)$ where $K(x)$ belongs to a polytope and $Z$ is a user-defined function. The proposed method is then extended to account for the presence of uncertainties and noisy data. Furthermore, a method to estimate the resulting region of attraction is given using only data. Simulation examples are used to illustrate the results and compare them to existing methods from the literature.

关键词

math.OCeess.SY

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