Cyber-security of centralized, decentralized, and distributed control-detector architectures for nonlinear processes

Abstract

Decentralized and distributed control systems provide an efficient solution to many challenges of controlling large-scale industrial processes. With the expansion in communication networks, vulnerability to cyber intrusions also increases. This work investigates the effect of different types of standard cyber-attacks on the operation of nonlinear processes under centralized, decentralized, and distributed model predictive control (MPC) systems. The robustness of the decentralized control architecture over distributed and centralized control architectures is analyzed. Moreover, a machine-learning-based detector is trained using sensor data to monitor the cyber security of the overall system. Specifically, detectors built using feed-forward neural networks are used to detect the presence of an attack or identify the subsystem being attacked. A nonlinear chemical process example is simulated to demonstrate the robustness of decentralized control architectures and the effectiveness of the neural-network detection scheme in maintaining the closed-loop stability of the system.