Software-Defined Event-Triggering Control for Large-Scale Networked Systems Subject to Stochastic Cyberattacks

This article is concerned with the problem of dynamic event‐triggered prescribed performance control for nonlinear systems under signal temporal logic tasks. By utilizing the method of prescribed performance control, the constrained plant can be transformed into an unconstrained one, and a dynamic event‐triggered feedback control law is generated for the transformed system to ensure that the signal temporal logic specification is satisfied. A dynamic event‐triggered mechanism is designed to guarantee the event‐triggered stability, safety and complex specification. Besides, Zeno phenomenon is definitely avoided. Compared with the continuous‐time feedback controller, the event‐triggered controller has proven to be effective in reducing sensing, communication and computation costs. Finally, two simulations are given to illustrate the effectiveness of theoretical results.

Section: Introductionmentioning

confidence: 99%

Dynamic event‐triggered prescribed performance control for nonlinear systems with signal temporal logic

Long,

Gao

2024

“…Therefore, the round‐robin protocol is introduced in order to fully utilize the resource. In Reference 26, it is claimed that an important issue in large‐scale networked control systems is how to guarantee the system performance under the limited network bandwidth and stochastic cyberattacks. These existing results inspire us to introduce an event‐triggered scheme into Kuramoto network synchronization control.…”

Section: Introductionmentioning

confidence: 99%

Fixed‐time Synchronization of Stochastic Kuramoto‐Oscillator Networks With Switching Topology Under Dynamic Event‐triggered Scheme

Wang,

Sun,

et al. 2024

This paper proposes a dynamic event‐triggered fixed‐time control scheme for the phase synchronization of Kuramoto networks with switching topology and stochastic coupling noise. To deal with the problems of switching topology and stochastic coupling noise, a multilayered control structure with a specially designed noise reduction layer is developed. The effectiveness is proved based on stochastic differential theory. To save communication resources and reduce control frequency, a dynamic event‐triggered mechanism is proposed. By introducing a dynamic variable into the triggering condition, the triggered instants are further reduced compared to the traditional static event‐triggered method. The synchronizing criteria and parameter conditions are established with stochastic Lyapunov stability theory. The analysis of Zeno behavior is also given. Finally, numerical simulation experiments are conducted to demonstrate the effectiveness of the proposed dynamic event‐triggered control scheme.

Privacy‐preserving‐based fuzzy filtering for nonlinear networked systems with adaptive‐event‐triggered mechanism and FDI attacks

Liu,

Tang,

Zha

et al. 2024

This article centers around the privacy‐preserving‐based secure filtering issue for interval type‐2 (IT‐2) fuzzy networked systems with false data injection (FDI) attacks. In order to achieve the goal of privacy preserving and significantly enhancing system security against potential eavesdropping threats, a novel encryption‐decryption mechanism (EDM) is adopted to safeguard the safety of signals across the network. The mechanism encrypts the transmitted signal by introducing artificial noise, secret key, and utilizing randomly selected nodes. This ensures that the actual transmitted data remains invisible to eavesdroppers while minimally the impact on the estimated performance of the proposed EDM. Given the network communication resources are becoming constrained due to the ever‐increasing network traffic, an adaptive event‐triggered mechanism (AETM) is employed to ease network congestion by an adaptively adjustable threshold. Then, various sufficient conditions have been outlined to ensure that the filtering error system meets the prescribed disturbance attenuation level. In the end, a numerical example is presented to evaluate both the precision and effectiveness of the developed algorithms.