A Versatile Framework for Data-Driven Control of Nonlinear Systems

This note aims to provide a systematic investigation of direct data-driven control, enriching the existing literature not by adding another isolated result, but rather by offering a unifying, versatile, and broad framework that enables the generation of novel results in this domain. We formulate the nonlinear design problem from a high-level perspective as a set of desired controlled systems and propose systematic procedures to synthesize data-driven control algorithms that meet the specified design requirements. Various examples are presented to demonstrate the applicability of the proposed approach and its ability to derive new insights and results, illustrating the novel contributions enabled by the framework.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Automatic Control
Published
2025-08-08
DOI
https://doi.org/10.1109/tac.2025.3597243
Citations
3
Primary Topic
Control Systems and Identification
Type
article
Field-Weighted Citation Impact
3.64
Controls
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article

A Versatile Framework for Data-Driven Control of Nonlinear Systems

Nima Monshizadeh, Pietro Tesi, Claudio De Persis
3 citations
IEEE Transactions on Automatic Control
Control Systems and Identification
3.64
article

A Versatile Framework for Data-Driven Control of Nonlinear Systems

Nima Monshizadeh, Pietro Tesi, Claudio De Persis
article en
3 citations

Abstract

This note aims to provide a systematic investigation of direct data-driven control, enriching the existing literature not by adding another isolated result, but rather by offering a unifying, versatile, and broad framework that enables the generation of novel results in this domain. We formulate the nonlinear design problem from a high-level perspective as a set of desired controlled systems and propose systematic procedures to synthesize data-driven control algorithms that meet the specified design requirements. Various examples are presented to demonstrate the applicability of the proposed approach and its ability to derive new insights and results, illustrating the novel contributions enabled by the framework.

IEEE Transactions on Automatic ControlVol. 71(1)
University of Groningen (NL), University of Florence (IT)
Openalex Percentile: Top 7%
Control Systems and Identification
3.64
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A Versatile Framework for Data-Driven Control of Nonlinear Systems — Nima Monshizadeh, Pietro Tesi, et al. · IEEE Transactions on Automatic Control (2025) | TGRS Research Map | TGRS