文献类型：期刊文献

英文题名：Data-Driven Predictive Control for Stochastic Descriptor Systems: An Innovation-Based Approach Handling Non-Causal Dependencies

作者：Ma, Yunxiang[1];Wang, Yibo[1];Li, Zhongmei[2];Shang, Chao[1]

机构：[1]Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Automat, Beijing 100084, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Minist Educ, Shanghai 200237, Peoples R China

年份：2026

卷号：10

起止页码：1291

外文期刊名：IEEE CONTROL SYSTEMS LETTERS

收录：EI(收录号：20262621007229);WOS:【ESCI(收录号:WOS:001811580000029)】；

基金：This work was supported by the National Natural Science Foundation of China under Grant 62373211 and Grant 62327807.

语种：英文

外文关键词：Technological innovation; Modeling; Noise; Predictive control; Matrices; Steady-state; Sequential analysis; Trajectory; Sequences; Stochastic systems; Descriptor system; data-driven predictive control; Kalman filter; innovation form

摘要：Descriptor systems arise naturally in real-world applications governed by algebraic constraints, such as power networks, robotics and chemical processes. When a descriptor model contains a nontrivial nilpotent block, the discrete-time input-output map may be improper: the current output depends on future inputs and, in the stochastic case, on future noise terms. This letter proposes a data-driven predictive control framework for stochastic descriptor systems that handles these non-causal dependencies without explicitly identifying system matrices. The key idea is to split fast subsystem into noise-driven and input-driven parts, and then combine the former with the slow subsystem such that an innovation-driven Kalman filter can be appropriately defined to reformulate the stochastic descriptor system into an innovation-driven form. Based on this, a new behavioral system representation is derived, which inspires a data-driven innovation-based multi-step output predictor and a practical Inno-DeePC algorithm that enables data-driven predictive control design without known system matrices while implicitly handling algebraic constraints. Numerical experiments on a DC microgrid demonstrate the effectiveness of the proposed approach.