基于Nussbaum的自适应神经网络跟踪控制非线性PDE-ODE系统受到欺骗攻击的欺骗攻击
IEEE transactions on cybernetics
|July 8, 2024
概括
本研究介绍了适应性神经网络 (NN),用于复杂的非线性偏微分方程-普通微分方程 (PDE-ODE) 系统中的跟踪控制. 该方法确保了系统的稳定性和性能,尽管对传感器和执行器的欺骗攻击.
科学领域:
- 控制系统工程 控制系统工程
- 应用数学 应用数学 应用数学
- 人工智能的人工智能
背景情况:
- 非线性偏微分方程-普通微分方程 (PDE-ODE) 合系统存在独特的控制挑战.
- 对传感器和执行器的欺骗攻击损害了这些系统中的状态/输出可用性.
- 现有的控制方法与PDEs的无限维性质以及攻击下的系统合作斗争.
研究的目的:
- 为非线性PDE-ODE合系统开发一种新的自适应神经网络 (NN) 追踪控制方案.
- 解决欺骗攻击所带来的挑战,使系统状态和输出不可用.
- 为了确保强大的跟踪性能和信号局限性,尽管在不利的条件下.
主要方法:
- 基于后退方法的新坐标转换被用来重新构建PDE子系统.
- 适应性神经网络 (NN) 用于处理未知的控制收益和非线性.
- 纳斯姆技术被纳入,以减轻攻击引入的不确定性影响.
主要成果:
- 拟议的控制方案保证了PDE-ODE合系统内的所有信号都将受到限制.
- 即使传感器和执行器受到欺骗攻击,也可以实现有效的跟踪控制性能.
- 模拟结果验证了开发的自适应控制策略的有效性.
结论:
- 新的自适应性NN跟踪控制方案对于面临欺骗攻击的非线性PDE-ODE合系统有效.
- 后退,NN和Nussbaum技术的整合提供了一个强大的解决方案.
- 该方法确保了系统稳定性和在传感器和执行器攻击下可靠的性能.
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