Abstract
In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.
Highlights
Road roughness is usually used to describe the degree of undulation of the road surface
According to the 7-DOF vehicle data, the MATLAB software is used for programming, and the vertical acceleration and elevation angular acceleration of the body of the B-level road surface, which are substituted to the trained network for calculation, are simulated as shown in Figures 7 and 8, respectively
According to the 7-DOF vehicle data, the MATLAB software is used for programming, and the vertical acceleration and elevation angular acceleration of the body of the C-level road surface, which are substituted to the trained network for Vertical acceleration of the body (m∙s-2)
Summary
Road roughness is usually used to describe the degree of undulation of the road surface. Liu and Cui [19] proposed a collaborative simulation algorithm for the performances between ride comfort and handling stability of a vehicle considering the connection and mutual influences. A small number of researchers had focused on the nonlinear model, and they had usually neglected the effect on solving methods from dynamic simulation and low-amplitude vibration ride comfort. Is paper is dedicated to solving the problem of road roughness identification of different road level, providing a theoretical and methodological basis for road unevenness identification with the NARX neural network. In the paper, based on the 7-DOF vehicle vibration model, the NARX neural network is used to establish identification vehicle responses which can provide a theoretical and methodological basis for practical application of the NARX neural network to identify road roughness.
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