Abstract
With the integration of internet of things (IoT) devices, cloud computing, and other digital technologies into chemical processes, the complexity and stealthiness of cyber-attacks have increased. To mitigate the impact of sensor cyber-attacks in chemical processes, this work presents a framework that develops physics-informed machine learning (PIML)-based detectors and resilient controllers for improving closed-loop performance of nonlinear system under cyber-attacks. The PIML detector is constructed through a customized loss function that integrates the domain knowledge of cyber-attacks into the training process. Additionally, upon detection of attacks, a knowledge-guided extended Kalman filter is developed to provide estimated states for resilient control prior to replacement by redundant sensors. A chemical process example is used to illustrate the application of the proposed PIML-based detection and resilient control methods to handle cyber-attacks.
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