基于数字孪生的不确定线性动态系统动态输出反馈镇定
Dynamic output-feedback stabilization of uncertain linear dynamics via digital twins
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中文总结 AI 辅助
该研究提出数字孪生框架，用于不确定动态系统的输出反馈镇定与参数辨识，通过虚实模型协同实现状态重构、参数估计与镇定控制，保障不确定性下的鲁棒性能。
中文摘要 AI 辅助
本研究提出一种用于不确定动态系统的输出反馈镇定与参数辨识的数字孪生框架。虚拟模型与物理过程并行演化，实时同化测量数据。该数字孪生可重构系统状态并生成镇定反馈，同时采用贝叶斯方法从受控动态数据中推断模型参数。耦合虚实动态的数值结果表明，数字孪生可同时作为观测器、参数估计器与控制主体，在不确定性下确保鲁棒性能。
英文摘要
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.
