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dkpy: Robust Control with Structured Uncertainty in Python

2025/11/17 by Adams, Timothy Everett, Dahdah, Steven, Forbes, James Richard
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #Stability and Control of Uncertain Systems #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2511.13927

openalex publication_date 2025/11/17 · openalex created_date 2025/11/20 · openalex updated_date 2026/07/28

Abstract

Models used for control design are, to some degree, uncertain. Model uncertainty must be accounted for to ensure the robustness of the closed-loop system. μ-analysis and μ-synthesis methods allow for the analysis and design of controllers subject to structured uncertainties. Moreover, these tools can be applied to robust performance problems as they are fundamentally robust control problems with structured uncertainty. The contribution of this paper is dkpy, an open-source Python package for performing robust controller analysis and synthesis for systems subject to structured uncertainty. dkpy also provides tools for performing model uncertainty characterization using data from a set of perturbed systems. The open-source project can be found at https://github.com/decargroup/dkpy.

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