Practical Learning-Tracking Framework Under Unknown Nonrepetitive Channel Randomness

• 成果类型: Article
• 署名作者: Shen, Dong
• 署名单位: Renmin University of China
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3184406
• 发表日期: 2023
• 页码: 3331-3347
• 关键词: estimation; Channel estimation; Additives; Fading channels; uncertainty; Random variables; CONVERGENCE; Consistent estimate; convergence analysis; Iterative learning control (ILC); nonrepetitive channel randomness; unbiased estimate

摘要

In this study, we consider the learning-tracking problem for stochastic systems through unreliable communication channels. The channels suffer from both multiplicative and additive randomness subject to unknown probability distributions. The statistics of this randomness, such as mean and covariance, are nonrepetitive in the iteration domain. This nonrepetitive randomness introduces nonstationary contamination and drifts to the actual signals, yielding essential challenges in signal processing and learning control. Therefore, we propose a practical framework constituted by an unbiased estimator of the mean inverse, a signal correction mechanism, and learning control schemes. The convergence and tracking performance are strictly established for both constant and decreasing step-lengths. If the statistics satisfy asymptotic repetitiveness in the iteration domain, a consistent estimator applies to the framework while retaining the framework's asymptotic properties. Illustrative examples are provided to verify the theoretical results.