Cooperative Robust Fault-Tolerant Control for Vehicular Platoon Systems With Performance Limitations and Various Uncertainties

Cooperative adaptive cruise control (CACC) of the vehicular platoon system is challenging due to simultaneous existences of heterogeneous performance limitations, time varying unknown leader input, and various uncertainties. To address this problem, this study introduces a new cooperative robust fault-tolerant control (CRFTC) approach to enhance the safety of CACC systems. First, an intermediate based cooperative estimation method is designed to simultaneously estimate the heterogeneous performance limitations of perception and actuation, matched disturbances, and leader's input for platoon members. Then, a distributed CRFTC method is developed to accommodate the effects of performance limitations, matched disturbances while suppressing the mismatched disturbances, so that car-following consistency can be achieved. Furthermore, optimal gains of the proposed CRFTC w.r.t. predecessor leader following (PLF), bi-directional (BD) and predecessor following (PF) communication network topologies are derived using linear matrix inequalities (LMIs). The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performances of the proposed CRFTC are analyzed based on Lyapunov stability theory. Finally, comparative simulations are conducted for CACC systems with PLF, BD and PF topologies to demonstrate the advantages of the CRFTC on improving the safety and stability.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2025-06-06
DOI
https://doi.org/10.1109/tvt.2025.3568494
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cooperative Robust Fault-Tolerant Control for Vehicular Platoon Systems With Performance Limitations and Various Uncertainties

Bo Wang, Hongmao Qin, Mingyang Chen, Zeyu Yang et al.
IEEE Transactions on Vehicular Technology
Traffic control and management
article

Cooperative Robust Fault-Tolerant Control for Vehicular Platoon Systems With Performance Limitations and Various Uncertainties

Bo Wang, Hongmao Qin, Mingyang Chen, Zeyu Yang, Yuan Chang, Ziniu Hu
article en

Abstract

Cooperative adaptive cruise control (CACC) of the vehicular platoon system is challenging due to simultaneous existences of heterogeneous performance limitations, time varying unknown leader input, and various uncertainties. To address this problem, this study introduces a new cooperative robust fault-tolerant control (CRFTC) approach to enhance the safety of CACC systems. First, an intermediate based cooperative estimation method is designed to simultaneously estimate the heterogeneous performance limitations of perception and actuation, matched disturbances, and leader's input for platoon members. Then, a distributed CRFTC method is developed to accommodate the effects of performance limitations, matched disturbances while suppressing the mismatched disturbances, so that car-following consistency can be achieved. Furthermore, optimal gains of the proposed CRFTC w.r.t. predecessor leader following (PLF), bi-directional (BD) and predecessor following (PF) communication network topologies are derived using linear matrix inequalities (LMIs). The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performances of the proposed CRFTC are analyzed based on Lyapunov stability theory. Finally, comparative simulations are conducted for CACC systems with PLF, BD and PF topologies to demonstrate the advantages of the CRFTC on improving the safety and stability.

IEEE Transactions on Vehicular TechnologyVol. 74(10)
Hunan University (CN), University College London (GB), Tsinghua University (CN)
Climate action
Openalex Percentile: Top 86%
Traffic control and management
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.