Robust Moving Horizon Estimation With Diverse Prior Predictions for Nonlinear Systems

• 成果类型: Article
• 署名作者: Arezki, H.; Zemouche, A.; Alessandri, A.
• 署名单位: University of Genoa; Universite de Lorraine; Universite de Lorraine; Centre National de la Recherche Scientifique (CNRS)
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3676243
• 发表日期: 2026
• 关键词: DISCRETE-TIME-SYSTEMS; TO-STATE STABILITY; Detectability; observers

摘要

This article addresses robust stability analysis for moving horizon estimation (MHE) in nonlinear systems and proposes new conditions for tuning the MHE cost function parameters. These conditions are directly linked to the MHE window size and the incremental exponential input/output-to-state stability (i-EIOSS) coefficients of the system. Our approach begins by introducing a general constraint equation within the MHE optimization program, combined with a filtering prior prediction technique. This equation incorporates an auxiliary dynamical system assumed to satisfy the i-EIOSS property. To further enhance the robustness of MHE stability conditions while reducing the required window size, we integrate this auxiliary system with advanced prediction techniques, marking a significant advancement in MHE design. Finally, comparative discussions and numerical evaluations are included to clarify the differences and connections between our proposed method and existing approaches in the literature.