Road profile identification using estimation techniques: comparison between independent component analysis and Kalman filter

Abstract

This paper focuses on the identification of a road profile disturbance acting on vehicles. Vehicles are subjected to many kinds of excitation sources such as road profile irregularities, which constitute a major area of interest when designing suspension systems. Indeed, determining the road profile is important for passive suspension design on the one hand and for determining an appropriate control law for active suspensions on the other. Direct measurement techniques of the road profile are expensive, so solutions based on estimation theory are needed. The aim of this paper is to characterize the road excitation using the Independent Component Analysis (ICA). This proposed method can reconstruct original excitation sources by using physically measurable signals of the system under study. Here, the estimation of road disturbances is considered as output sources and identified from dynamic responses of the vehicle. These responses can be measured via sensors or can be numerically computed. In our case, they are numerically simulated using the Newmark method and consider different types of road profiles. The obtained results are validated after using a comparison with the Kalman filtering. The robustness of the ICA is confirmed via parametric study.

Robustness study of the road profile estimation technique under uncertainty

“…We mentioned that some pre-treatments: centering and whitening (Chaabane et al, 2019) are applied to the vector{V observed }. Thus, [M unmixing ] is determined and finally, {S estimated } will be equal to…”

“…In a real context, the suspension system model is multi-dimensional so its parameters are uncertain due to variation of the sprung mass, tire stiffness and damping. In this context, Chaabane et al (2019) proposed a comparison between the augmented Kalman filter estimation technique and the Independent Component Analysis (ICA) method. The authors showed that the Kalman filtering displayed greater sensitivity to both sprung mass and vehicle speed variations.…”

“…The authors showed that the Kalman filtering displayed greater sensitivity to both sprung mass and vehicle speed variations. However, it was remarked that the Kalman filter displayed certain losses in the frequency spectrum mainly at high speeds (Chaabane et al, 2019) compared to the ICA. So, for an accurate road profile estimation, the uncertain parameters of the vehicle model have to be taken into account.…”