Design of Intelligent Control Under Machine Learning Supervision and Signal Compression Mechanism Design for NCSs Under DoS Attacks
Xiao Feng Cai, Kaibo Shi, Yanbin Sun, Jinde Cao, Oh‐Min Kwon, Cheng Qiao, Zhihong Tian · IEEE Transactions on Intelligent Transportation Systems · 2024
This short paper addresses the challenge of network congestion in T-S fuzzy networked control systems (NCSs) caused by denial of service (DoS) attacks and quality of service (QoS) queuing mechanisms. Firstly, a novel signal compression mechanism is introduced to mitigate network congestion. The trigger threshold is optimized using a mini-batch descent gradient algorithm, effectively reducing bandwidth utilization. Furthermore, tailored Lyapunov-Krasovskii functions (LKFs) are established for the system, and we propose an intelligent event-triggered controller (IETC) under machine learning supervision. Finally, the effectiveness of the proposed approach is demonstrated through rigorous verification on the joint CarSim-Simulink platform.