UKF‐based adaptive variable structure observer for vehicle sideslip with dynamic correction

Chen, Jie; Song, Jian; Zhang, Lipeng; Jia, Gang; Xu, Ruidong; Cai, Yang

doi:10.1049/iet-cta.2015.1030

Cited by 49 publications

(48 citation statements)

References 37 publications

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“…An effective solution to reduce the computational burden while keeping a high accuracy in the VSA estimation is the use of the unscented Kalman filter (UKF) instead of the EKF [33][34][35][36][37][38][39]. As a matter of fact, many recent observer-based studies on VSA estimation mostly focused on UKF [33,35,37,38].…”

Section: Kalman Filtermentioning

confidence: 99%

“…As a matter of fact, many recent observer-based studies on VSA estimation mostly focused on UKF [33,35,37,38].…”

Section: Kalman Filtermentioning

confidence: 99%

“…Moreover, in [35] Chen et al shows a further improvement of UKF, where an UKF-based adaptive variable structural observer with dynamic correction has been presented. Basically, model uncertainty has been compensated with adaptive parameters, while a variable structure observer has been realised through using a sensor-based observer to compensate errors occurring in non-linear regions.…”

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%

“…As a matter of fact, many recent observer-based studies on VSA estimation mostly focused on UKF [33,35,37,38].…”

Section: Kalman Filtermentioning

confidence: 99%

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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“…Many scholars have investigated the strategies for vehicle state estimation based on the KF and its improved algorithms [5][6][7][8][9][10]. Gadola et al [11] combined the extended Kalman filter (EKF) with a 2-degree-of-freedom (2-DOF) single-track vehicle model and the simplified Magic formula tire model to obtain the sideslip angle.…”

Section: Introductionmentioning

confidence: 99%

Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter

Chen

Guo

et al. 2020

Applied Sciences

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Reliable vehicle motion states are critical for the precise control performed by vehicle active safety systems. This paper investigates a robust estimation strategy for vehicle motion states by feat of the application of the extended set-membership filter (ESMF). In this strategy, a system noise source is only limited as unknown but bounded, rather than the Gaussian white noise claimed in the stochastic filtering algorithms, such as the unscented Kalman filter (UKF). Moreover, as one part of this strategy, a calculation scheme with simple structure is proposed to acquire the longitudinal and lateral tire forces with acceptable accuracy. Numerical tests are carried out to verify the performance of the proposed strategy. The results indicate that as compared with the UKF-based one, it not only has higher accuracy, but also can provide a 100% hard boundary which contains the real values of the vehicle states, including the vehicle's longitudinal velocity, lateral velocity, and sideslip angle. Therefore, the ESMF-based strategy can proffer a more guaranteed estimation with robustness for practical vehicle active safety control.

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“…However, it is no easy work to get the real-time information of all of the wheel pressure. There are several studies dedicated to this topic and researchers have made some progress in this area [15][16][17]. However, pressure estimation is very difficult and sometimes not suitable for pressure control due to estimation error.…”

Section: Introductionmentioning

confidence: 99%

Unified Brake Service by a Hierarchical Controller for Active Deceleration Control in an Electric and Automated Vehicle

et al. 2017

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Abstract:Unified brake service is a universal service for generating certain brake force to meet the demand deceleration and is essential for an automated driving system. However, it is rather difficult to control the pressure in the wheel cylinders to reach the target deceleration of the automated vehicle, which is the key issue of the active deceleration control system (ADC). This paper proposes a hierarchical control method to actively control vehicle deceleration with active-brake actuators. In the upper hierarchical, the target pressure of wheel cylinders is obtained by dynamic equations of a pure electric vehicle. In the lower hierarchical, the solenoid valve instructions and the pump speed of hydraulic control unit (HCU) are determined to satisfy the desired pressure with the feedback of measured wheel cylinder pressure by pressure sensors. Results of road experiments of a pure electric and automated vehicle indicate that the proposed method realizes the target deceleration accurately and efficiently.

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UKF‐based adaptive variable structure observer for vehicle sideslip with dynamic correction

Cited by 49 publications

References 37 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

Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter

Unified Brake Service by a Hierarchical Controller for Active Deceleration Control in an Electric and Automated Vehicle

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