适应性弹性神经控制不确定的时间延迟非线性CPS与完全状态约束下的欺骗攻击欺骗攻击
Zhihao Chen1, Xin Wang2, Ning Pang1
1WESTA College, Southwest University, Chongqing 400700, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究为面临欺骗攻击和状态约束的不确定的网络物理系统 (CPS) 提出了一个适应性弹性控制策略. 控制器确保系统稳定性和状态约束满足,尽管未知攻击和时间延迟.
科学领域:
- 控制系统工程 控制系统工程
- 网络物理系统安全 网络物理系统安全
- 非线性系统理论 非线性系统理论
背景情况:
- 网络物理系统 (CPS) 容易受到未知的时间变化的欺骗攻击和状态约束.
- 来自攻击的传感器干扰模糊了系统状态变量,使控制设计复杂化.
- 传统的控制方法可能会与计算复杂性和未知的系统动态作斗争.
研究的目的:
- 为不确定的时间延迟的非线性CPS开发一种适应性弹性控制策略.
- 为应对未知欺骗攻击和全州约束所带来的挑战.
- 确保系统稳定性和在极限条件下的性能.
主要方法:
- 一种新的后退控制策略,使用受损变量和动态表面技术.
- 引入攻击补偿器来抵消未知的攻击信号.
- 对于状态变量约束,利用屏障莱普诺夫函数 (BLF).
- 使用辐射基函数 (RBF) 神经网络对未知非线性项的近似计算.
- 应用Lyapunov-Krasovskii函数 (LKF) 来处理未知时间延迟项.
主要成果:
- 设计一个适应性弹性控制器,保证系统状态变量的趋同.
- 确保满足CPS的预定义状态约束.
- 证明所有闭环系统信号的半全球均最终边界性.
- 通过数值模拟实验验证理论结果的验证.
结论:
- 拟议的适应性弹性控制策略有效地管理在欺骗攻击和状态约束下不确定的时间延迟非线性CPS.
- 控制器确保系统稳定性和遵守状态限制.
- 先进的控制技术的结合为安全的CPS操作提供了强大的解决方案.
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