在网络攻击下对非线性MIMO系统进行低复杂度双层代学习控制
IEEE transactions on cybernetics
|December 2, 2025
概括
本研究为面临虚假数据注入 (FDI) 攻击的非线性系统引入了一种双层代学习控制 (DLILC). 该方法提高了跟踪准确性和系统安全性,防止网络威胁.
科学领域:
- 控制工程 控制工程 控制工程
- 网络安全 网络安全
- 系统动力学系统动力学
背景情况:
- 重复的非线性多输入多输出 (MIMO) 系统需要强大的跟踪控制.
- 假数据注入 (FDI) 攻击对系统性能和安全构成重大威胁.
- 现有的代学习控制 (ILC) 方法可能会与非线性和网络攻击作斗争.
研究的目的:
- 开发一个安全和有效的代学习控制 (ILC) 策略,用于非线性MIMO系统在FDI攻击下.
- 提高跟踪精度和系统抵御动态非线性和网络威胁的弹性.
- 为了减少对预定义的系统参数和计算负载的依赖.
主要方法:
- 实施双层代学习控制 (DLILC) 方法.
- 开发一个外部循环自适应设定点调机制,以优化动态增益.
- 在内部循环中使用比例导数 (PD) 控制器,以及用于非线性转换的双动态线性化.
- 构建基于输出观察者的实时补偿器,以减轻外国直接投资攻击的影响.
主要成果:
- 在重复的非线性MIMO系统中实现了高精度的跟踪性能.
- 与传统方法相比,显著减少了计算负担.
- 经过验证的优越弹性和有效缓解虚假数据注入 (FDI) 攻击.
- 展示了学习收益的动态优化,减少对预设参数的依赖.
结论:
- 拟议的DLILC方法为控制面临外国直接投资攻击的非线性MIMO系统提供了安全有效的解决方案.
- 适应性设定点调整和基于观察者的补偿有效地提高了系统的稳定性和安全性.
- 这项工作为在复杂的动态系统中推进安全的代学习控制提供了一条新的途径.
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