Abstract
Networked Control Systems (NCSs) are increasingly deployed in critical infrastructure, yet their performance is often compromised by communication impairments such as time-varying delays and packet loss. These disturbances can undermine stability, reliability, and overall efficiency. To address these challenges, this study proposes an AI-augmented supervisory control framework that integrates communication metrics directly into the control strategy rather than relying solely on conventional error signals. The architecture employs machine learning classifiers—specifically K-Nearest Neighbors (KNN) and Random Forest (RF)—to assess real-time network conditions (normal, degraded, and emergency) and dynamically adjust the parameters of a core PID controller. Under degraded conditions, PID gains are attenuated to enhance robustness against latency, while in emergency scenarios, a conservative safe-mode configuration is activated to preserve plant stability under severe packet loss. Comparative analysis demonstrates that Random Forest, due to its ensemble-based design, achieves superior situational awareness, smoother mode transitions, and more reliable global stability than KNN. Moreover, RF reduces volatile parameter updates, thereby protecting physical actuators from excessive stress and extending component lifespans. The findings highlight the potential of ensemble learning approaches to enhance resilience and operational safety in delay-prone NCS environments.