Cooperative DYC system design for optimal vehicle handling enhancement

Tamaddoni, Seyed Hossein; Taheri, Saied; Ahmadian, Mehdi

doi:10.1109/acc.2010.5531266

Cited by 6 publications

(1 citation statement)

References 10 publications

(13 reference statements)

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“…Finally, simulations and other tests are conducted to verify the fault-tolerant system's efficacy and real-time reaction in various driving conditions. The findings show that our suggested method can keep longitudinal speed inaccuracy (below 3%) and lateral stability within acceptable limits, hence enhancing vehicle safety [18].…”

Section: Literature Reviewmentioning

confidence: 78%

An Autonomous Vehicle Stability Control Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network

2022

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Due to instability issues in autonomous vehicles, the risk of danger is increasing rapidly. These problems arise due to unwanted faults in the sensor or the actuator, which decrease vehicle efficiency. In this modern era of autonomous vehicles, the risk factor is also increased as the vehicles have become automatic, so there is a need for a fault-tolerant control system (FTCS) to avoid accidents and reduce the risk factors. This paper presents an active fault-tolerant control (AFTC) for autonomous vehicles with a fuzzy neural network that can autonomously identify any wheel speed problem to avoid instability issues in an autonomous vehicle. MATLAB/Simulink environment was used for simulation experiments and the results demonstrate the stable operation of the wheel speed sensors to avoid accidents in the event of faults in the sensor or actuator if the vehicle becomes unstable. The simulation results establish that the AFTC-based autonomous vehicle using a fuzzy neural network is a highly reliable solution to keep cars stable and avoid accidents. Active FTC and vehicle stability make the system more efficient and reliable, decreasing the chance of instability to a minimal point.

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Section: Literature Reviewmentioning

confidence: 78%