在受到攻击的情况下,分析和优化非线性描述器系统中基于观察者的安全滑动模式控制.
IEEE transactions on cybernetics
|January 21, 2026
概括
本研究引入了使用模糊模型和Q学习的弹性控制框架,用于面临攻击的网络物理系统 (CPS). 该方法通过适应性优化对网络物理威胁的控制策略来提高安全性和性能.
科学领域:
- 控制系统工程 控制系统工程
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 网络物理系统 (CPS) 容易受到传感器和执行器攻击,特别是在通信限制下.
- 非线性描述器系统需要强大的控制策略,以在网络物理威胁期间保持稳定性和性能.
- 现有的安全框架通常在复杂的攻击场景下与自适应控制和优化作斗争.
研究的目的:
- 为非线性描述符网络物理系统在通信限制和网络物理攻击下提出一个弹性控制框架.
- 开发一种自适应的模糊滑动模式观察器 (SMO) 用于估计具有不匹配前提变量的受损系统状态.
- 设计一个滑动模式控制器 (SMC),确保闭环可接受性和滑动表面可达性,使用秘书鸟优化算法 (SBOA) 进行优化.
主要方法:
- 将Takagi-Sugeno (T-S) 模糊模型与基于Q学习的事件触发机制 (ETM) 集成.
- 适应性模糊滑动模式观察器 (SMO) 和滑动模式控制器 (SMC) 的开发.
- 应用秘书鸟优化算法 (SBOA) 来优化控制器和观察者收益以解决非凸的优化问题.
主要成果:
- 拟议的框架有效地平衡了运营效率与对网络物理威胁的强有力的保护.
- 适应性模糊SMO准确地估计了受损的系统状态,即使有不匹配的前提变量.
- 在卡车拖车系统上的模拟验证了这种方法在各种攻击场景下保持系统稳定性和性能方面的有效性.
结论:
- 开发的弹性控制框架显著提高了网络环境中的非线性网络物理系统的安全性.
- 模糊逻辑,Q学习和滑动模式控制的集成,通过SBOA进行优化,为自适应性安全提供了新的解决方案.
- 这项工作有助于保护CPS免受复杂的网络物理攻击,确保可靠的系统运行.
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