A cyber‐secure control‐detector architecture for nonlinear processes
A cyber‐secure control‐detector architecture for nonlinear processes
复制标题
用于非线性过程的网络安全控制检测器架构
DOI：
10.1002/aic.16907
复制
发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
P. Christofides
中科院分区：
文献类型：
--
作者：
Scarlett Chen;Zhe Wu;P. Christofides
这项工作提出了一种检测器集成的两层控制体系结构，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。对于一类一般的非线性系统，上层基于lyapunov的模型预测控制器（LMPC）使用网络传感器测量来提高闭环性能，与下层网络安全显式反馈控制器相结合，将非线性多变量过程驱动到稳态。尽管网络传感器测量可能容易受到网络攻击，但双层控制架构确保了该过程不会受到破坏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机器学习方法，即人工神经网络（ANN），在标称和噪声操作条件下使用传感器测量开发的，并在线应用于模拟反应堆-反应堆-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方面的有效性。在成功检测到网络攻击后，两层控制架构允许方便地重新配置控制系统，以稳定过程到其运行稳态。
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.