Event-Based H∞ Fuzzy Control for Implicit Hybrid AVS Systems Embedding Acceleration Characteristic Under Random Deception and DoS Attacks

This article considers the problem of event-based $H_{\infty }$ fuzzy control for implicit hybrid active vehicle suspension systems (IHAVSSs) embedding acceleration and Markovian jumping characteristics under random deception and denial-of-service (DoS) attacks. Aiming at conserving of communication resources, an event-triggered strategy is introduced in the communication link between sensor and controller. The accelerations of sprung, unsprung and tyre are taken into account, the active vehicle suspension systems are constructed as the implicit one. Admissibility conditions are derived under the framework of linear matrix inequalities by utilizing the piecewise Lyapunov-Krasovskii (L-K) functional. Via singular value decomposition (SVD) technique, the IHAVSSs are proved to be regular and impulse-free. By means of Takagi-Sugeno (T-S) fuzzy method and parallel-distributed compensation (PDC) technique, a mode-dependent (M-D) fuzzy feedback controller (FFC) is designed to ensure $H_{\infty }$ stochastic admissibilization of IHAVSSs under random deception and DoS attacks. The validity of the results is demonstrated by using the quarter active vehicle suspension model produced by Quanser company. Note to Practitioners—The active vehicle suspension systems hold paramount significance in practical applications, effectively dampening vibrations from road surfaces and enhancing ride comfort. Concurrently, as networking advances, interconnectedness among various components within the active vehicle suspension system grows. However, the communication links are vulnerable to malicious attacks that may decrease the performance of the AVSSs, which inspires our current research. This paper delves into the $H_{\infty }$ stochastic admissibilization of the AVSSs subjected to network malicious attacks, and makes full use of the dynamic variable of the system in the modeling process, that is, the accelerations of each components are selected as the system state variable. The method is applied to a platform simulation, and the stochastic admissibility of implicit hybrid active vehicle suspension systems under random deception and DoS attacks is realized.