Dynamic output-feedback stabilization of uncertain linear dynamics via digital twins

2026/07/29 by Philipp A. Guth, Karl Kunisch, Sergio S. Rodrigues +1
Mathematics · #math.OC #msc:93C40 #msc:93B52 #msc:49N10 #msc:93B51

paper · pdf

31 pages, 8 figures

arxiv created 2026/07/29 · arxiv updated 2026/07/30

Abstract

This work presents a digital twin framework for output-feedback stabilization and parameter identification in uncertain dynamical systems. A virtual model evolves in parallel with the physical process, assimilating measurement data in real time. By design, the digital twin reconstructs the system state and generates a stabilizing feedback, while model parameters are simultaneously inferred from data of the controlled dynamics using a Bayesian approach. Numerical results for the coupled physical-virtual dynamics demonstrate how digital twins can act jointly as observers, parameter estimators, and control agents, ensuring robust performance under uncertainty.

Citations

• A design specification for Critical Illness Digital Twins to cure sepsis: responding to the National Academies of Sciences, Engineering and Medicine Report: Foundational Research Gaps and Future Directions for Digital Twins
• Mathematical Opportunities in Digital Twins (MATH-DT)
• Stabilization of uncertain linear dynamics: an offline-online strategy
• Sequential learning and control: Targeted exploration for robust performance
• A Historical Perspective of Adaptive Control and Learning
• A Bayesian adaptive ensemble Kalman filter for sequential state and\n parameter estimation

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