This article mainly studies the quantized data-based iterative learning tracking control (QDBILTC) problem of nonlinear networked control systems in the presence of signals quantization and denial-of-service (DoS) attacks. The quantizer considered here is static with the logarithmic form. First, an estimate output attack compensation mechanism is designed to compensate for the effect of DoS attacks based on the extended dynamic linearization method. Then, a QDBILTC algorithm is developed to guarantee the system tracking performance and the bounded input and bounded output stability in mean-square sense. The process of designing the QDBILTC algorithm only uses the input and output data of the system, and the proof of which uses the compression mapping principle and the mathematical induction. The effectiveness of the proposed QDBILTC algorithm is illustrated by a digital simulation.

中文翻译：

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拒绝服务攻击下基于量化数据的迭代学习控制

本文主要研究存在信号量化和拒绝服务 (DoS) 攻击时非线性网络控制系统的量化数据迭代学习跟踪控制 (QDBILTC) 问题。这里考虑的量化器是静态的，具有对数形式。首先，基于扩展动态线性化方法设计估计输出攻击补偿机制来补偿DoS攻击的影响。然后，开发了一种QDBILTC算法来保证系统的跟踪性能和均方意义上的有界输入和有界输出稳定性。QDBILTC算法的设计过程仅使用系统的输入输出数据，其证明采用压缩映射原理和数学归纳法。