发表机构
Indian Institute of Technology Madras（印度马德拉斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对变量含误差框架下的线性描述符系统辨识问题，扩展SMI-IPCA方法至行为设定，无需先验变量分类与系统指数结构假设，可估计噪声方差等参数，经仿真验证方法有效。
AI 中文摘要
从受噪声污染的数据中辨识线性描述符系统（微分代数方程，DAE）通常有两个关键假设：需先验地将变量分类为输入和输出，且需预先指定系统指数的结构假设。本文提出一种数据驱动方法，用于在变量含误差框架内辨识指数-0和指数-1型DAE。我们将基于子空间的迭代主成分分析（SMI-IPCA）方法扩展至行为设定，将所有测量变量视为统一的增广向量，以避免分类偏差。该方法可系统估计噪声方差、代数输出变量和微分输出变量的数量，同时在无需先验结构知识的情况下，辨识代数约束及对应最小实现阶的动态系统核表示。对指数-0和指数-1系统的仿真研究验证了该方法的有效性及实用性。
英文摘要
The identification of linear descriptor systems (DAEs) from noise-corrupted data makes two critical assumptions: requirement of an \textit{a priori} classification of variables into inputs and outputs, and a pre-specified structural assumption with respect to the index of the system. This paper proposes a data-driven methodology for identifying index-0 and index-1 DAEs within an errors-in-variables framework. We extend a subspace-based iterative PCA (SMI-IPCA) approach to the behavioral setting, treating all measured variables as a unified augmented vector to avoid classification bias. This method enables systematic estimation of the noise variances, the number of algebraic and differential output variables, while simultaneously identifying the algebraic constraints and kernel representation of the dynamic system corresponding to its minimal realization order without prior structural knowledge. Simulation studies on index-0 and index-1 systems demonstrate the effectiveness of the proposed approach and its practical applicability.
CommentsAccepted for publication in the 8th International Symposium on Advanced Control of Industrial Processes (IEEE AdCONIP 2026), University of Auckland, New Zealand. There are 6 pages and 3 figures in this draft
