Effective measurement and estimation of vehicle state parameters plays a crucial role in vehicle stability control. Among them, the measurement cost of vehicle sideslip angle is higher, and the value obtained by integral calculation in the actual test includes noise, so the accuracy is difficult to be guaranteed. In this paper, a neural network method is proposed to identify the system, and a system model of vehicle sideslip angle identification is established based on the vehicle body state quantity (yaw rate, longitudinal acceleration, lateral acceleration, etc.) which can be easily obtained. According to the experimental conditions, vehicle state parameters were obtained based on CarSim, and Matlab was used for network training. Finally, the network is verified, the applicability of the trained network under different working conditions is discussed, and some assumptions about the optimization of this method are put forward.
Estimation of Vehicle Centroid Side Angle Based on Neural Network
Lect. Notes Electrical Eng.
13.01.2022
18 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Estimation of Vehicle Centroid Side Angle Based on Neural Network
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