Robust Vehicle Sideslip Estimation Based on Kinematic Considerations

Selmanaj, Donald; Corno, Matteo; Panzani, Giulio; Savaresi, Sergio M.

doi:10.1016/j.ifacol.2017.08.2513

Cited by 27 publications

(14 citation statements)

References 14 publications

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“…For instance, Madhusudhanan et al extended the vehicle model with pitch and roll motions [31, 32]. In order to cope with sensor noises, Selmanaj et al [33, 34] employed a heuristic scheduling method to improve the robustness of a kinematics‐based estimator.…”

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

Sideslip angle estimation of ground vehicles: a comparative study

Liu

Wang

Walker

2020

IET Control Theory & Appl

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Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially‐available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state‐of‐the‐art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics‐, dynamics‐ and neural network‐based estimators. A hardware‐in‐loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics‐based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics‐based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network‐based estimator can perform well in all manoeuvres once the training datasets are properly selected.

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Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

Sideslip angle estimation of ground vehicles: a comparative study

Liu

Wang

Walker

2020

IET Control Theory & Appl

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“…The main issue of VSA estimation using a kinematic model is that it does not work when the vehicle yaw rate is relatively small or zero, as the system becomes unobservable [116,117].…”

Section: Vehicle Modelsmentioning

confidence: 99%

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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“…Although it can be directly measured by sensors such as optical sensors [5] or GPS sensors [6], [7], these solutions are not implemented by OEMs because of cost and reliability. Therefore, the estimation of sideslip angle based on the sensors available in production vehicles is an important topic that has been widely discussed in the literature [8]- [24]. Most of the approaches in the literature are model-based and can be classified into three main categories: kinematics modelbased, dynamics model-based and a combination of the two.…”

Section: Introductionmentioning

confidence: 99%

“…The kinematics model-based approach proposed in [8], [9] constructs an observer based on the longitudinal and lateral translation kinematics of a point mass model. This method has the advantage of not requiring the vehicle parameters, tire model and road friction coefficient.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Approach to Real-Time Estimation of Vehicle Sideslip, Road Bank Angles, and Sensor Bias

Liao

Borrelli

2019

IEEE Trans. Veh. Technol.

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Robust estimation of vehicle sideslip angle is essential for stability control applications. However, the direct measurement of sideslip angle is expensive for production vehicles. This paper presents a novel sideslip estimation algorithm which relies only on sensors available on passenger and commercial vehicles. The proposed method uses both kinematics and dynamics vehicle models to construct extended Kalman filter observers. The estimate relies on the results provided from the dynamics model observer where the tire cornering stiffness parameters are updated using the information provided from the kinematics model observer. The stability property of the proposed algorithm is discussed and proven. Finally, multiple experimental tests are conducted to verify its performance in practice. The results show that the proposed approach provides smooth and accurate sideslip angle estimation. In addition, our novel algorithm provides reliable estimates of bank angles and lateral acceleration sensor bias.Index Terms-sideslip angle estimation, bank angle estimation, extended Kalman filter, recursive robust least square

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Robust Vehicle Sideslip Estimation Based on Kinematic Considerations

Cited by 27 publications

References 14 publications

Sideslip angle estimation of ground vehicles: a comparative study

Sideslip angle estimation of ground vehicles: a comparative study

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

An Adaptive Approach to Real-Time Estimation of Vehicle Sideslip, Road Bank Angles, and Sensor Bias

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