Decentralized Event-Triggered Model Predictive Control for Fuzzy Cyber-physical Systems Under Optimal Jamming Attacks

In this article, a decentralized event-triggered scheme (DETS)-based model predictive control (MPC) strategy is investigated for a nonlinear cyber-physical system (CPS) under jamming attacks. The nonlinear plant is described by an interval type-2 Takagi-Sugeno fuzzy model and the system measurements are grouped into several nodes. First, a DETS is proposed to determine whether the measured signals are transmitted. By the DETS, the measured signals are released asynchronously, which are more flexible. Moreover, the jamming attacks are supposed to be energy limited in the multiple wireless transmission channels. Under jamming attacks, the signal-to-interference-plus-noise ratio will be degraded and the measured signals will be lost. To maximize the damaging effect, an optimal power allocation strategy of jammer is designed. Considering that the system states are unmeasurable, an observer-based MPC algorithm is proposed, which consists of an off-line designed secure state observer and an on-line optimized MPC strategy. Besides, the recursive feasibility of presented algorithm and the stability of fuzzy CPS are ensured. Finally, two illustrative examples are given to show the validity and superiority of proposed method.