Online Identification of Time-Varying Systems Using Excitation Sets and Change-Point Detection

• 成果类型: Article
• 署名作者: Leung, Chi Ho; Hota, Ashish R.; Pare, Philip E.
• 署名单位: Purdue University System; Purdue University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kharagpur
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3649189
• 发表日期: 2026
• 关键词: RECURSIVE LEAST-SQUARES; persistency; inference; epidemics

摘要

In this work, we first show that the problem of parameter identification is often ill-conditioned and lacks the persistence of excitation required for the convergence of online learning schemes. To tackle these challenges, we introduce the notion of optimal and greedy excitation sets, which contain data points with sufficient richness to aid in the identification task. We then present the greedy excitation set-based recursive least squares algorithm to alleviate the problem of the lack of persistent excitation, and prove that the iterates generated by the proposed algorithm minimize an auxiliary weighted least squares cost function. When data points are generated from time-varying parameters, online estimators tend to underfit the true parameter trajectory, and their predictability deteriorates. To tackle this problem, we propose a memory resetting scheme leveraging change-point detection techniques. Finally, we illustrate the performance of the proposed algorithms via a numerical case study to learn the time-varying parameters of networked epidemic dynamics, and compare it to results obtained using conventional approaches.