Road surface unevenness identification method based on Kalman filtering theory

Abstract

The invention discloses a road surface unevenness identification method based on a Kalman filtering theory, and belongs to the technical field of vehicle road unevenness identification in vehicle engineering. Firstly, road contour recognition is defined as a semi-vehicle model inverse problem in a state space, and vertical acceleration signals of the vehicle body, front wheels and rear wheels are collected on a calibrated road surface to serve as measurement data of the method; the collected data is inputted into an established road surface unevenness recognition algorithm to obtain dynamic response of the vehicle so as to reversely deduce road surface unevenness information, wherein the road surface unevenness identification algorithm is established based on the Kalman filtering theory. Only one kind of measurement data such as acceleration signals needs to be collected, the acceleration sensor is easy to arrange, the hardware cost is low, and operability is high; the method not only can be used for predicting the road excitation of the vehicle in the early stage of vehicle design, but also can calculate the response of the vehicle to any given vehicle speed.

Description

technical field [0001] The invention belongs to the technical field of vehicle road roughness recognition in vehicle engineering, and in particular relates to a road surface roughness recognition method based on Kalman filter theory. Background technique [0002] Road surface roughness is an important input affecting vehicle dynamics, especially for some special vehicles, it may lead to fatigue failure of components or decrease of ride comfort. Road surface information is essential for road quality assessment, road roughness index calculation, vehicle dynamics analysis, suspension design, and control system development. However, for technical and economical reasons these signals cannot be measured in standard vehicles and must therefore be identified using special methods. Identifying the excitation acting on the system from the given response of the system is a so-called inverse problem, which is usually an ill-posed problem. [0003] In order to facilitate road maintenan...

AI Technical Summary

Problems solved by technology

However, due to the high price, the application of the profiler in ordinary vehicles is limited

(Kim, 2002) studied the profiling method based on visual inspection, but this method is limited in rainy weather

With the development of artificial intelligence methods, some scholars have used neural network models to identify road unevenness, but neural network methods require a long calculation time due to the complexity of the model (Mahdi et al., 2010; et al., 2012; Ngwangwa et al., 2010)

(Kim et al., 2002; Imine et al., 2006) propose a model-based sliding mode observer approach, which leads to long computation time for complex models

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