Intelligent Event-Triggered Control Supervised by Mini-Batch Machine Learning and Data Compression Mechanism for T-S Fuzzy NCSs Under DoS Attacks

This paper presents a comprehensive solution to mitigate network congestion in T-S fuzzy networked control systems (NCSs) caused by denial-of-service (DoS) attacks and quality-of-service (QoS) queuing mechanisms. We develop a novel data compression mechanism to alleviate network congestion and use a mini-batch descent gradient algorithm to optimize trigger thresholds, thereby reducing bandwidth usage. Additionally, we introduce asymmetric Lyapunov-Krasovskii functions (LKFs) to decrease the number of decision variables, which improves the reliability and robustness of the control algorithm. Finally, we propose an intelligent event-triggered controller (IETC) supervised by mini-batch machine learning and validate it on the joint CarSim-Simulink platform. Experimental results demonstrate that our approach reduces the sensitivity of autonomous vehicle (AV) systems to network fluctuations while ensuring system stability under network congestion caused by DoS attacks.