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- W4293232199 abstract "It is well known that the knowledge of dynamic states is crucial for electronic stability control (ESC) and active safety system of automotive vehicles. In this paper, a novel method is established for the estimation of side slip angle in the presence of measurement noises and different road frictions. To begin with, a vehicle dynamic model and a tire model are exploited to reveal the relationship between tire road friction coefficient (TRFC) and sideslip angle. Furthermore, in order to eliminate the sensors error in the inertial measurement unit, a recurrent neural network is deployed by modeling the lateral motion and sensors measurement. Unlike conventional methods, a modified Sliding Mode Observer (MSMO) is proposed to manage the uncertainty brought by road conditions. Finally, the presented method is validated by co-simulations of Carsim and Matlab. The results show that the developed approach is robust to the abnormal measurement noise and different road friction coefficients." @default.
- W4293232199 created "2022-08-27" @default.
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- W4293232199 date "2022-07-01" @default.
- W4293232199 modified "2023-10-16" @default.
- W4293232199 title "Estimation of Vehicle Sideslip Angle based on Modified Sliding Mode Observer and Recurrent Neural Network" @default.
- W4293232199 doi "https://doi.org/10.1109/acirs55390.2022.9845624" @default.
- W4293232199 hasPublicationYear "2022" @default.
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