This paper studies iterative learning control (ILC) using faded measurements without system information. The measurements are transmitted through fading channels, where the fading phenomenon is modelled by a multiplicative random variable. The system matrices are assumed unknown a priori and a random difference technique is applied to estimate the gradient using the available tracking data. An online ILC algorithm is established with strict convergence analysis along the iteration axis, followed by practical variants and discussions. The generated input sequence is proved to converge to the desired one in the almost sure sense. Illustrative simulations are presented to verify the theoretical results.

中文翻译：

---

使用没有系统信息的衰落测量的迭代学习控制：梯度估计方法

本文使用没有系统信息的衰减测量来研究迭代学习控制 (ILC)。测量值通过衰落信道传输，其中衰落现象由乘法随机变量建模。假设系统矩阵是先验未知的，并且应用随机差分技术来使用可用的跟踪数据估计梯度。建立在线 ILC 算法，沿迭代轴进行严格的收敛分析，然后是实际变体和讨论。生成的输入序列被证明几乎可以肯定地收敛到所需的序列。给出了说明性的模拟来验证理论结果。