Stability Analysis of Networked Control Systems Under DoS Attacks and Security Controller Design With Mini-Batch Machine Learning Supervision

Abstract

This study investigates the stability problem in nonlinear networked control systems (NCSs). First, innovative compression rules are introduced to mitigate network congestion and bandwidth utilization issues stemming from quality of service (QoS) queuing mechanisms and denial of service (DoS) attacks. We develop an intelligent trigger controller supervised by a mini-batch machine learning (MBML) algorithm to optimize network bandwidth utilization. Furthermore, we formulate more generalized Lyapunov-Krasovskii functions (LKFs) to simplify mathematical derivations, and we employ appropriate integral inequalities to minimize constraints. Finally, experimental evaluations are conducted on an autonomous vehicle (AV) using the joint CarSim-Simulink platform to verify the effectiveness of the proposed intelligent trigger controller.