在假数据注入攻击下,基于自适应重建模型的预测控制对网络随机系统进行预测控制
相关实验视频
IEEE transactions on cybernetics
|March 4, 2026
概括
本研究介绍了一种弹性随机模型预测控制 (MPC) 方法,用于保护网络系统免受虚假数据注入 (FDI) 攻击. 这种新的方法通过重建控制输入和调整系统参数来提高安全性和效率.
科学领域:
- 控制系统工程 控制系统工程
- 网络安全 网络安全
- 网络化系统 网络化系统
背景情况:
- 网络系统容易受到虚假数据注入 (FDI) 攻击,影响控制性能.
- 现有的弹性模型预测控制 (MPC) 方法通常表现为保守主义和高资源消耗.
- 对于应对外国直接投资攻击,静态MPC框架尚未得到充分的探索.
研究的目的:
- 为面临外国直接投资攻击的网络系统提出一种新的弹性随机MPC方法.
- 为了减轻保守主义和减少当前弹性控制策略对资源的需求.
- 加强网络控制系统在网络威胁下的安全性和可靠性.
主要方法:
- 开发一种适应性输入重建机制,以缓解外国直接投资的攻击能量假设.
- 适应性预测地平线和终端约束的共同设计,以尽量减少计算复杂性.
- 将硬约束转化为随机约束,以减轻保守主义.
主要成果:
- 导出足够的条件以确保递归可行性和闭环稳定性.
- 提出的方法有效地重建了在FDI攻击下可行的控制输入.
- 在直流-直流转换器系统上的模拟证明了该方法的有效性.
结论:
- 拟议的弹性随机MPC方法提供了针对外国直接投资攻击的强有力的解决方案.
- 适应性输入重建和约束设计减少了保守主义和计算负载.
- 该框架提高了联网随机系统的安全性和性能.
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