Fuzzy Filtering for Networked Nonlinear Systems with Dynamic Quantization: Quantization-Dependent Event-Triggered Case
- 1School of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
- 2School of Electronics and Information Engineering, Langfang Normal University, Langfang 065000, China
Abstract
This paper investigates the event-triggered robust dissipative filtering problem for discrete-time networked nonlinear systems subject to dynamic quantization and stochastic cyber attacks. The networked nonlinear system under consideration is represented by a Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties. A dynamic quantizer and a novel quantization-dependent event-triggered mechanism are introduced to reduce the communication burden, thereby improving the utilization of the limited network resources. Moreover, a Bernoulli-distributed random variable is adopted to characterize the effects of stochastic cyber attacks. Attention is focused on the design of a filter such that the filtering error system is stochastically stable and satisfies a prescribed dissipative performance level. By applying the matrix inequality decoupling approach, the design conditions for the desired dynamic quantizer and filter are obtained in terms of linear matrix inequalities. Finally, simulation results for the Henon mapping system demonstrate that the proposed design approach guarantees the prescribed dissipative performance of γ=1.5. In addition, the number of triggering events is reduced by 14.13% compared with that of existing quantization-independent methods.
Related
- Event-Triggered Funnel Control for Output Reference Tracking of Unknown Nonlinear Systems
- Finite-Dimensional Recursions for Small-Noise Expansions in Nonlinear Filtering
- Uniqueness and stability of nonlinear filtering equations with unbounded random coefficients
- Attack-Aware Event-Triggered Impulsive Control for Switched Nonlinear Systems via Multi-Diffeomorphism
- Finite Cumulant Towers for Small-System-Noise Expansions in Nonlinear Filtering
BibTeX
@article{guo2026fuzzy,
title = {{Fuzzy Filtering for Networked Nonlinear Systems with Dynamic Quantization: Quantization-Dependent Event-Triggered Case}},
author = {Yunbo Guo and Kezheng Cheng and Zengliang Zhang and Zhimin Li},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14213973},
url = {https://www.mdpi.com/2227-7390/14/21/3973},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Fuzzy Filtering for Networked Nonlinear Systems with Dynamic Quantization: Quantization-Dependent Event-Triggered Case
AU - Yunbo Guo
AU - Kezheng Cheng
AU - Zengliang Zhang
AU - Zhimin Li
PY - 2026
DA - 2026/10/31
JO - Mathematics
DO - 10.3390/math14213973
UR - https://www.mdpi.com/2227-7390/14/21/3973
AB - This paper investigates the event-triggered robust dissipative filtering problem for discrete-time networked nonlinear systems subject to dynamic quantization and stochastic cyber attacks. The networked nonlinear system under consideration is represented by a Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties. A dynamic quantizer and a novel quantization-dependent event-triggered mechanism are introduced to reduce the communication burden, thereby improving the utilization of the limited network resources. Moreover, a Bernoulli-distributed random variable is adopted to characterize the effects of stochastic cyber attacks. Attention is focused on the design of a filter such that the filtering error system is stochastically stable and satisfies a prescribed dissipative performance level. By applying the matrix inequality decoupling approach, the design conditions for the desired dynamic quantizer and filter are obtained in terms of linear matrix inequalities. Finally, simulation results for the Henon mapping system demonstrate that the proposed design approach guarantees the prescribed dissipative performance of γ=1.5. In addition, the number of triggering events is reduced by 14.13% compared with that of existing quantization-independent methods.
SN - 2227-7390
ER -