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- W4385226928 abstract "Accurate and reliable sensor information is crucial for intelligent vehicles to ensure safety and reliability. It is significant to devise effective solutions to diagnose possible sensor faults. In this paper, a sensor fault detection, isolation, and estimation approach is developed for intelligent vehicle-integrated motion systems. The Luenberger observer is designed and embedded into the fault diagnosis framework to real-time estimate the system states. Interval observers are constructed for vehicle subsystems to estimate the interval bounds of the corresponding observation errors. The estimation errors enter into the corresponding intervals generated by the adaptive laws when the sensor fault is free. In the event of a sensor fault, it can be detected efficiently and the faulty sensor can be isolated. Moreover, occurred sensor fault can be estimated by modifying the interval observer structure. The simulation results in the standard J-Turn test scenario are provided to evaluate and verify the effectiveness of the proposed method." @default.
- W4385226928 created "2023-07-25" @default.
- W4385226928 creator A5013194131 @default.
- W4385226928 creator A5046849667 @default.
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- W4385226928 date "2023-10-01" @default.
- W4385226928 modified "2023-10-18" @default.
- W4385226928 title "A sensor fault detection, isolation, and estimation method for intelligent vehicles" @default.
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- W4385226928 doi "https://doi.org/10.1016/j.conengprac.2023.105620" @default.
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