This work presents a detector‐integrated two‐tier control architecture capable of identifying the presence of various types of cyber‐attacks, and ensuring closed‐loop system stability upon detection of the cyber‐attacks. Working with a general class of nonlinear systems, an upper‐tier Lyapunov‐based Model Predictive Controller (LMPC), using networked sensor measurements to improve closed‐loop performance, is coupled with lower‐tier cyber‐secure explicit feedback controllers to drive a nonlinear multivariable process to its steady state. Although the networked sensor measurements may be vulnerable to cyber‐attacks, the two‐tier control architecture ensures that the process will stay immune to destabilizing malicious cyber‐attacks. Data‐based attack detectors are developed using sensor measurements via machine‐learning methods, namely artificial neural networks (ANN), under nominal and noisy operating conditions, and applied online to a simulated reactor‐reactor‐separator process. Simulation results demonstrate the effectiveness of these detection algorithms in detecting and distinguishing between multiple classes of intelligent cyber‐attacks. Upon successful detection of cyber‐attacks, the two‐tier control architecture allows convenient reconfiguration of the control system to stabilize the process to its operating steady state.

中文翻译：

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非线性过程的网络安全控制检测器架构

这项工作提出了一种检测器集成的两层控制体系结构，该体系结构能够识别各种类型的网络攻击的存在，并在检测到网络攻击时确保闭环系统的稳定性。与一般类别的非线性系统一起使用，基于Lyapunov的上层模型预测控制器（LMPC）使用网络传感器测量来改善闭环性能，并与较低层的网络安全显式反馈控制器结合使用，以驱动非线性多变量过程达到稳态。尽管网络传感器的测量可能容易受到网络攻击的影响，但两层控制体系结构可确保该过程不受不稳定的恶意网络攻击的影响。基于数据的攻击检测器是通过传感器学习方法通​​过机器学习方法开发的，即人工神经网络（ANN），在正常和嘈杂的运行条件下，并在线应用于模拟反应器-反应器-分离器过程。仿真结果证明了这些检测算法在检测和区分多类智能网络攻击中的有效性。成功检测到网络攻击后，两层控制体系结构可以方便地对控制系统进行重新配置，以将过程稳定在其运行稳定状态。