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GA-Enhanced Control for Autonomous Vehicles: Coordinating FlexRay Protocol Under Randomly Perturbed Sampling Periods

Aogui Hu et al.

IEEE Transactions on Cybernetics2026https://doi.org/10.1109/tcyb.2026.3653814article
AJG 3
Weight
0.50

What the paper says

This article addresses the lateral dynamics control problem for autonomous vehicle systems under randomly perturbed sampling (RPS) periods and the FlexRay communication protocol. To capture vehicle nonlinearities under variable-velocity conditions, a T-S fuzzy model is constructed using longitudinal velocity as the premise variable. The random sampling behavior caused by hardware aging and environmental disturbances is modeled as a Markovian process. Then, measured outputs are transmitted under the FlexRay protocol (FRP) that integrates both time-driven (static) and event-driven (dynamic) scheduling characteristics. By fully analyzing the situation of static and dynamic scheduling, a unified compensation strategy is employed to build a new switching output model reflecting the impact of the FRP on the measured outputs. Based on this output model, a sampling-mode-dependent fuzzy controller is designed to handle random sampling and hybrid scheduling issues, which results in a membership asynchronous phenomenon between the autonomous vehicle model and controller. By using the asynchronous constraint technique, sufficient conditions with low conservatism are derived to guarantee stochastic stability and $H_{\infty }$ performance of the closed-loop system. Furthermore, a comprehensive optimization problem (OP) is established, and a corresponding genetic algorithm (GA) is presented to provide a solution-solving scheme. Simulation results confirm the effectiveness and superiority of the proposed control strategy under complex communication environments.

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https://doi.org/https://doi.org/10.1109/tcyb.2026.3653814

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@article{aogui2026,
  title        = {{GA-Enhanced Control for Autonomous Vehicles: Coordinating FlexRay Protocol Under Randomly Perturbed Sampling Periods}},
  author       = {Aogui Hu et al.},
  journal      = {IEEE Transactions on Cybernetics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tcyb.2026.3653814},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.