Abstract
Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.
Highlights
IntroductionOne of the main causes of accidents in road transport is the loss of vehicle stability
One of the main causes of accidents in road transport is the loss of vehicle stability.vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover
Different sensors were installed in the vehicle, such as an MSW 250 Nm steering angle sensor from Kistler (2), a Vbox 3i dual antenna from Racelogic (3) which utilizes two global positioning system (GPS)/GLONASS
Summary
One of the main causes of accidents in road transport is the loss of vehicle stability. In [7,10,11], the Kalman filter estimates the vehicle roll angle These algorithms do not consider that the parameters of the vehicle model can change, since they might be time-dependent. The dual Kalman filter (DKF) is used to simultaneously obtain an estimation of states and of parameters [12,13,14,15]. The novelty of this paper is to design an observer to estimate on-line the vehicle roll angle and vehicle’s parameters This observer integrates neural networks (NN) and a PDF dual Kalman filter. This estimator uses NN to calculate the “pseudo-roll angle” which is introduced as an input into the constrained DKF.
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