Dynamic-Memory Event-Based Adaptive Sliding-Mode Control for Networked Nonlinear Implicit Hybrid Systems Under Random Spoofing Attacks and Data-Buffer- Aided Mechanism
Jingjuan Zhu, Guangming Zhuang, Jianwei Xia, Xiangpeng Xie
Abstract
In this article, we investigates the issue of dynamic-memory event-based adaptive sliding-mode control for fuzzy-model-based nonlinear implicit Markov jump systems. The communication networks are deemed to be invaded by spoofing attacks in a probabilistic manner, where normally transmitted data may be replaced. Depending on the data-buffer-aided mechanism, a novel dynamic-memory event-triggered protocol is proposed to modulate the transmission frequency by drawing into historically released packets and two auxiliary internal dynamic variables. Then, a suitable event-triggered adaptive sliding-mode control strategy is developed to actualize the reachability of the specified integral sliding surface, which is capable of diminishing the impacts of model uncertainties. Stochastic admissibility and strict dissipativity conditions of closed-loop fuzzy implicit Markov jump systems are derived under the framework of linear matrix inequalities. Finally, a networked truck–trailer reversing system is exploited to verify the validity of developed strategies.