Nonasymptotic and Robust Distributed Algebraic Estimation for Linear Time-Varying Systems

• 成果类型: Article
• 署名作者: Zhang, Yu-Qing; Liu, Da-Yan; Boutat, Driss; Wu, Ze-Hao
• 署名单位: Universite de Orleans; Foshan University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3519037
• 发表日期: 2025
• 页码: 3386-3393
• 关键词: estimation; sensors; Noise measurement; vectors; Time-varying systems; Sensor phenomena and characterization; observability; Sensor systems; observers; noise; Algebraic estimation method; derivative estimation; linear time-varying system; nonasymptotic distributed state estimation; reduced-order state estimator; Robust Estimation

摘要

This article investigates the nonasymptotic and robust distributed algebraic state estimation for linear time-varying systems using a network of sensors in noisy environments. First, at each sensor node, the system state is transformed into a linear combination of a group of nodes' local observable states, allowing for distributed estimation by estimating a reduced-order state for each node. Second, without requiring initial conditions, the estimation scheme based on generalized modulating functions is employed to estimate each node's local observable state variables through algebraic integral formulas, which are robust against corrupting noises. Subsequently, leveraging the obtained distributed state expression and cross-agent communications with networked delays, a nonasymptotic and robust distributed state estimator is designed. Furthermore, an estimation error bound in noisy cases is provided. Finally, two simulation examples are presented to illustrate the effectiveness of our proposed approach.