发表机构
University of Kansas; Michigan State University（堪萨斯大学; 密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对非线性随机状态空间模型，提出序贯化集成卡尔曼反演方法，利用演化集成和隐式粒子滤波实现流式数据下的递归参数辨识，并在强非线性基准和锂离子电池模型上验证了准确性。
AI 中文摘要
在非线性随机状态空间模型中进行递归参数辨识具有挑战性，因为未知参数通过潜在状态动力学影响测量值。本文开发了一种序贯化集成卡尔曼反演方法，用于从流式测量中进行递归参数辨识。所提方法通过演化集成表示参数后验，并在新测量值可用时对其进行序贯更新。对于每个参数集成成员，使用隐式粒子滤波来近似参数校正所需的预测观测统计量。该公式使得递归参数学习成为可能。该方法在一个强非线性基准上进行了评估，并与几种现有方法进行了比较，随后应用于非线性双电容锂离子电池模型。数值结果表明了准确的递归参数辨识和潜在状态估计。
英文摘要
Recursive parameter identification in nonlinear stochastic state-space models is challenging because unknown parameters affect the measurements through latent-state dynamics. This paper develops a sequentialized ensemble Kalman inversion method for recursive parameter identification from streaming measurements. The proposed method represents the parameter posterior by an evolving ensemble and updates it sequentially as new measurements become available. For each parameter ensemble member, implicit particle filtering is used to approximate the predictive observation statistics required for parameter correction. This formulation enables recursive parameter learning. The method is evaluated on a strongly nonlinear benchmark, with comparison to several existing methods, and then on a nonlinear double-capacitor lithium-ion battery model. The numerical results demonstrate accurate recursive parameter identification and latent-state estimation.
CommentsAccepted for publication in the 2026 65th IEEE Conference on Decision and Control (CDC)
