Structure Identification of NDS with Descriptor Subsystems under Asynchronous, Non-Uniform, and Slow-Rate Sampling

This paper extends previous identification method to the asynchronous sampling scenario, enabling the simultaneous handling of asynchronous, non-uniform, and slow-rate sampling conditions. Moving beyond lumped systems, the proposed framework targets the identification of interconnection structure of Networked Dynamic Systems (NDS) with descriptor-form subsystems. In the first stage, right tangential interpolations are estimated from steady-state outputs, allowing all asynchronous samples to be fused into a unified estimator. In the second stage, a left null-space projection is employed to decouple the bilinear dependence between state-related matrices and interconnection parameters, reducing the identification problem to two successive linear estimation problems. The proposed approach eliminates the full-normal-rank transfer matrix assumption required in previous work, while providing theoretical guarantees of mean-square consistency and asymptotic unbiasedness. Numerical results demonstrate that the framework can accurately recover the system structure, even under severe sampling irregularities.