Dynamic output-feedback stabilization of uncertain linear dynamics via digital twins
arXiv:2607.26995
The paper proposes a digital‑twin framework that runs alongside an uncertain linear system, using real‑time data to estimate the system state and parameters while generating a stabilizing output‑feedback control law.
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.
31 pages, 8 figures