Observer‐Based Adaptive Event‐Triggered H∞$$ {H}_{\infty } $$ Control for Nonlinear Networked Control Systems Under Hybrid Attacks and Its Application

ABSTRACT This paper studies the design of an observer‐based event‐triggered controller for nonlinear networked control systems (NNCSs), utilizing the Takagi‐Sugeno (T‐S) fuzzy model to establish the system's nonlinear term. The influence of denial of service (DoS) attacks and deception attacks (DAs) is considered in the context of network communication processes. An improved adaptive event‐triggered mechanism (AETM) is implemented within the sensor and observer channels to optimize the utilization of network communication resources. The issue of event‐triggered delay is considered and resolved through the application of a piecewise Lyapunov functional and a free‐weighting matrix approach. The sufficient conditions for the system to satisfy the linear matrix inequalities (LMIs) under the control performance index are given in the form of two theorems, ensuring the system's mean square asymptotically stable (MSAS). Finally, a grid‐connected inverter (GCI) model is proposed, and the feasibility of the design method and its applicability in the actual system model is verified by simulation.

Authors

Institutions

Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2025-03-11
DOI
https://doi.org/10.1002/rnc.7913
Citations
3
Primary Topic
Stability and Control of Uncertain Systems
Type
article
Field-Weighted Citation Impact
3.64
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Observer‐Based Adaptive Event‐Triggered H∞$$ {H}_{\infty } $$ Control for Nonlinear Networked Control Systems Under Hybrid Attacks and Its Application

Xiao‐Heng Chang, Likun Hou
3 citations
International Journal of Robust and Nonlinear Control
Stability and Control of Uncertain Systems
3.64
article

Observer‐Based Adaptive Event‐Triggered H∞$$ {H}_{\infty } $$ Control for Nonlinear Networked Control Systems Under Hybrid Attacks and Its Application

Xiao‐Heng Chang, Likun Hou
article en
3 citations

Abstract

ABSTRACT This paper studies the design of an observer‐based event‐triggered controller for nonlinear networked control systems (NNCSs), utilizing the Takagi‐Sugeno (T‐S) fuzzy model to establish the system's nonlinear term. The influence of denial of service (DoS) attacks and deception attacks (DAs) is considered in the context of network communication processes. An improved adaptive event‐triggered mechanism (AETM) is implemented within the sensor and observer channels to optimize the utilization of network communication resources. The issue of event‐triggered delay is considered and resolved through the application of a piecewise Lyapunov functional and a free‐weighting matrix approach. The sufficient conditions for the system to satisfy the linear matrix inequalities (LMIs) under the control performance index are given in the form of two theorems, ensuring the system's mean square asymptotically stable (MSAS). Finally, a grid‐connected inverter (GCI) model is proposed, and the feasibility of the design method and its applicability in the actual system model is verified by simulation.

International Journal of Robust and Nonlinear ControlVol. 35(10)
Bohai University (CN)
Openalex Percentile: Top 8%
Stability and Control of Uncertain Systems
3.64
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Observer‐Based Adaptive Event‐Triggered H∞$ {H}_{\infty } $ Control for Nonlinear Networked Control Systems Under Hybrid Attacks and Its Application — Xiao‐Heng Chang, Likun Hou · International Journal of Robust and Nonlinear Control (2025) | TGRS Research Map | TGRS