Development and validation of a Kalman filter-based model for vehicle slip angle estimation

Gadola, Marco; Chindamo, Daniel; Romano, Marcello; Padula, Fabrizio

doi:10.1080/00423114.2013.859281

Cited by 80 publications

(60 citation statements)

References 11 publications

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“…That is, due to simplified tyre models that operate only in the linear zone (adapting the cornering stiffness to expand its usage range) but neglecting the non-linear and saturation zone behaviour. In a similar work [24], the tyre model adaption regards the entire non-linear range. By using a standard family of curves representing a generic Pacejka model dataset and an integrated tuning procedure for the EKF observer, the authors managed to develop a tyre model that was consistent with the lateral behaviour.…”

Section: Kalman Filtermentioning

confidence: 99%

“…• the use of single track vehicle models (also known as the bicycle model), e.g., [16,24,120] by Cheli et al, Gadola et al and Naets et al; • the assumption of the availability of the vehicle longitudinal speed, so that the first equation in Equation (7) is not used, e.g., [16,24,26] A set of equations similar to Equation (7) needs to be coupled with a tyre model, so as to express the forces as functions of the relevant slip parameters, e.g., longitudinal slip ratio and slip angle, respectively for longitudinal and lateral forces. In the literature, there are several approaches, e.g., the use of linear models, Pacejka models (also known as Magic Formula models), alternative tyre models (e.g., rational tyre model [122], Dugoff model [123], Burckhardt model [124]).…”

Section: Kalman Filtermentioning

confidence: 99%

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On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

2018

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Typical active safety systems that control the dynamics of passenger cars rely on the real-time monitoring of the vehicle sideslip angle (VSA), together with other signals such as the wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, which is known as the yaw rate. The VSA (also known as the attitude or "drifting" angle) is defined as the angle between the vehicle's longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory; therefore, it is a vital piece of information enabling directional stability assessment, such as in transience following emergency manoeuvres, for instance. As explained in the introduction, the VSA is not measured directly for impracticality, and it is estimated on the basis of available measurements such as wheel velocities, linear and angular accelerations, etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e., observer-based and neural network-based, focussing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. The advantages and limitations of each technique have been highlighted and discussed.

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Section: Kalman Filtermentioning

confidence: 99%

Section: Kalman Filtermentioning

confidence: 99%

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

2018

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“…In the latest study, I found that most of the vehicle models estimating vehicle state based on the EKF, cubature Kalman filter (CKF), or unscented Kalman filter (UKF) algorithm use a simple motorcycle model, a simple motorcycle model with the tire magic formula, or a simple 3-DOF vehicle model, which do not take into account the effects of vehicle body roll motion. 29,30 So, a nonlinear 3-DOF vehicle model including the longitudinal, lateral, and yaw direction is proposed for estimating the road curvature in real time considering roll motion of vehicle body (see Figure 5). …”

Section: -25mentioning

confidence: 99%

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

Chen

Zhang

Yue-Zhou

et al. 2017

Advances in Mechanical Engineering

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In order to resolve the goal confusion or missing on curve road, a target recognition method on automobile cruise system is put forward based on filtering and fuzzy control theory. A vehicle model with 3 degrees of freedom on the yaw rate, sideslip angle, and body roll angle is discretized and an extended Kalman filter is proposed for estimating the road curvature. For the key target on the straight road, a multi-object changing-lane fuzzy controller is presented to prejudge the dangerous changing-lane objects. For the recognition of key target on the curve road, a recognition model is set up to calculate the lateral offset between the estimated road curvature radius and the target trajectory radius. In order to validate the feasibility of the key target recognition method, the simulation cases are given for multi-objects on the curve road and straight road adopting Carsim and MATLAB/Simulink co-simulation. The simulation results show that designed fuzzy controller has good robustness for prejudging the dangerous changing-lane objects on straight road. The extended Kalman filter has realized the efficient estimation on road curvature and the recognition model has identified the key target accurately to prevent the goal confusion or missing on curve road. The research results can offer the theory reference for improving or perfecting the control performances of automobile intelligent cruise system.

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“…Several side-slip angle estimation strategies have been proposed in literature. They are mainly based on Extended Kalman Filters (EKF) [3][4][5]. Since EKF typically requires a reference vehicle model, it is able to provide a good estimation only if model parameters are accurately known.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network

Chindamo

Gadola

2018

MATEC Web of Conferences

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Abstract. In this work, a reliable and effective method to predict the vehicle side-slip angle is given by means of an artificial neural network. It is well known that artificial neural networks are a very powerful modelling tool. They are largely used in many engineering fields to model complex and strongly non-linear systems. For this application, the network has to be as simple as possible in order to work in real-time within built-in applications such as active safety systems. The network has been trained with the data coming from a custom manoeuvre designed in order to keep the method simple and light from the computational point of view. Therefore, a 5-10-1 (input-hidden-output layer) network layout has been used. These aspects allow the network to give a proper estimation despite its simplicity. The proposed methodology has been tested by means of the CarSim ® simulation package, which is considered one of the reference tools in the field of vehicle dynamics simulation. To prove the effectiveness of the method, tests have been carried out under different adherence conditions.

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Development and validation of a Kalman filter-based model for vehicle slip angle estimation

Cited by 80 publications

References 11 publications

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network

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