Data-Driven-Based Event-Triggered Resilient Control for Cigarette Weight Control System Under Denial of Service Attacks and False Data Injection Attacks

Abstract

This article proposes an event-triggered resilient control algorithm for cigarette weight control systems to defend against denial of service attacks (DoS) attacks and false data injection (FDI) attacks. First, the mathematical model of the system is derived by considering its physical and electromagnetic characteristics. Then, an attack detection mechanism is designed to identify potential FDI attacks. A predictive compensation algorithm is also developed to mitigate the influence of these attacks, incorporating an observer to estimate unknown compensation signals. Due to limited network resources, we employ an event-based model-free adaptive control (MFAC) framework, where data is transmitted only when specific conditions are violated. Throughout the entire control process, only the input and output data of the system are used. Finally, simulation comparisons are provided to demonstrate the effectiveness and superiority of our method.

Keywords

• Weight control systems
• DoS attacks
• FDI attacks
• model free adaptive control (MFAC)
• predictive algorithm