AI 中文总结
本文提出基于Ring-LWE的加密ILC框架，通过值域分解抑制误差增长、密文打包提升计算效率，实现网络控制系统重复跟踪任务的安全高效控制。
AI 中文摘要
本文针对网络控制系统中的重复跟踪任务，提出了一种基于环学习 with 错误（Ring-LWE）的加密迭代学习控制（ILC）框架。该架构将加密动态反馈控制器与加密ILC计算相集成。在每次试验中，反馈控制器在密文域中进行评估，加密的输出轨迹直接存储在云端。每次试验后，云端会评估跟踪误差，并基于存储的密文执行ILC计算，从而使被控对象侧无需存储累积的试验数据。所提框架为密文乘法采用了不同的打包参数，使云端能够在不解密的情况下同时处理低维输出反馈控制和高维ILC计算。尽管基于Ring-LWE的加密控制中的误差增长通常会被闭环稳定性抑制，但临界稳定的ILC迭代会导致注入的误差持续累积。为应对这一挑战，在加密ILC公式中引入了值域分解，以支持在无限次更新下进行评估。数值仿真表明，值域分解可抑制加密引起的扰动，而密文打包则提高了加密ILC更新的计算效率。
英文摘要
This paper proposes a Ring Learning With Errors (Ring-LWE) based encrypted iterative learning control (ILC) framework for repetitive tracking tasks over networked control systems. The architecture integrates an encrypted dynamic feedback controller with an encrypted ILC computation. During each trial, the feedback controller is evaluated in the ciphertext domain, and the encrypted output trajectory is stored directly in the cloud. After each trial, the cloud evaluates the tracking error and performs the ILC computation from the stored ciphertexts, so that the plant side does not need to store the accumulated trial data. The proposed framework uses distinct packing parameters for ciphertext multiplication, allowing the cloud to handle both lower-dimensional output feedback control and higher-dimensional ILC computation without decryption. While error growth in Ring-LWE based encrypted control is generally suppressed by closed-loop stability, the marginally stable ILC iterations cause the injected errors to accumulate continuously. To address this challenge, a range-space decomposition is introduced in the encrypted ILC formulation to allow evaluation under unlimited updates. Numerical simulations show that the range-space decomposition suppresses encryption-induced perturbation, while ciphertext packing improves the computational efficiency of the encrypted ILC update.
Comments6 pages, 3 figures. Accepted to ICCAS 2026
