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Journal articles on the topic 'Sideslip estimation'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Fan, Xiao Bin, and Pan Deng. "Study of Vehicle Sideslip Angle Real-Time Estimation Method." Advanced Materials Research 846-847 (November 2013): 26–29. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.26.

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In the vehicle stability control and other active safety systems, vehicle sideslip angle real-time estimation is necessary. However, the direct measurement of sideslip angle is more difficult or too costly, so it is often used in estimating methods. The vehicle sideslip angle of closed-loop Luenberger observer and Kalman observer were constructed based on two degrees of freedom bicycle model, as well as the direct integration method for large sideslip angle conditions. The comparative study showed that Kalman filtering estimation method and Luenberger estimation methods have better estimation

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2

Singh, Kanwar Bharat. "Virtual sensor for real-time estimation of the vehicle sideslip angle." Sensor Review 40, no. 2 (2019): 255–72. http://dx.doi.org/10.1108/sr-11-2018-0300.

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Purpose The vehicle sideslip angle is an important state of vehicle lateral dynamics and its knowledge is crucial for the successful implementation of advanced driver-assistance systems. Measuring the vehicle sideslip angle on a production vehicle is challenging because of the exorbitant price of a physical sensor. This paper aims to present a novel framework for virtually sensing/estimating the vehicle sideslip angle. The desired level of accuracy for the estimator is to be within +/− 0.2 degree of the actual sideslip angle of the vehicle. This will make the precision of the proposed estimato

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Chen, Te, Long Chen, Xing Xu, Yingfeng Cai, Haobin Jiang, and Xiaoqiang Sun. "Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion." Mathematical Problems in Engineering 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/9075372.

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Accurate estimation of longitudinal force and sideslip angle is significant to stability control of four-wheel independent driven electric vehicle. The observer design problem for the longitudinal force and sideslip angle estimation is investigated in this work. The electric driving wheel model is introduced into the longitudinal force estimation, considering the longitudinal force is the unknown input of the system, the proportional integral observer is applied to restructure the differential equation of longitudinal force, and the extended Kalman filter is utilized to estimate the unbiased l

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Wei, Wang, Bei Shaoyi, Zhang Lanchun, Zhu Kai, Wang Yongzhi, and Hang Weixing. "Vehicle Sideslip Angle Estimation Based on General Regression Neural Network." Mathematical Problems in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3107910.

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Aiming at the accuracy of estimation of vehicle’s mass center sideslip angle, an estimation method of slip angle based on general regression neural network (GRNN) and driver-vehicle closed-loop system has been proposed: regarding vehicle’s sideslip angle as time series mapping of yaw speed and lateral acceleration; using homogeneous design project to optimize the training samples; building the mapping relationship among sideslip angle, yaw speed, and lateral acceleration; at the same time, using experimental method to measure vehicle’s sideslip angle to verify validity of this method. Estimati

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Xia, Qiu, Long Chen, Xing Xu, et al. "Running States Estimation of Autonomous Four-Wheel Independent Drive Electric Vehicle by Virtual Longitudinal Force Sensors." Mathematical Problems in Engineering 2019 (June 9, 2019): 1–17. http://dx.doi.org/10.1155/2019/8302943.

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Exact sideslip angle estimation is significant to the dynamics control of four-wheel independent drive electric vehicles. It is costly and difficult-to-popularize to equip vehicular sensors for real-time sideslip angle measurement; therefore, the reliable sideslip angle estimation method is investigated in this paper. The electric driving wheel model is proposed and applied to the longitudinal force estimation. Considering that electric driving wheel model is a nonlinear model with unknown input, an unknown input estimation method is proposed to facilitate the longitudinal force observer desig

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6

Righetti, Giovanni, and Basilio Lenzo. "A Combined Dynamic–Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle." Applied Sciences 15, no. 3 (2025): 1365. https://doi.org/10.3390/app15031365.

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In modern automotive engineering, accurate vehicle sideslip angle estimation is crucial for enhancing vehicle safety, performance, and driver comfort. This paper addresses the challenge of estimating sideslip angle, an essential parameter for advanced driver-assistance systems (ADAS) and autonomous driving technologies. This study introduces a combined dynamic–kinematic extended Kalman filter (DK-EKF) approach that leverages the strengths of both kinematic and dynamic models while mitigating their individual limitations. The proposed DK-EKF enhances observability in low yaw rate conditions, a

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Chen, Te, Long Chen, Xing Xu, Yingfeng Cai, Haobin Jiang, and Xiaoqiang Sun. "Sideslip Angle Fusion Estimation Method of an Autonomous Electric Vehicle Based on Robust Cubature Kalman Filter with Redundant Measurement Information." World Electric Vehicle Journal 10, no. 2 (2019): 34. http://dx.doi.org/10.3390/wevj10020034.

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Accurate and reliable estimation information of sideslip angle is very important for intelligent motion control and active safety control of an autonomous vehicle. To solve the problem of sideslip angle estimation of an autonomous vehicle, a sideslip angle fusion estimation method based on robust cubature Kalman filter and wheel-speed coupling relationship is proposed in this paper. The vehicle dynamics model, tire model, and wheel speed coupling model are established and discretized, and a robust cubature Kalman filter is designed for vehicle running state estimation according to the discrete

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8

Singh, Kanwar Bharat. "Vehicle Sideslip Angle Estimation Based on Tire Model Adaptation." Electronics 8, no. 2 (2019): 199. http://dx.doi.org/10.3390/electronics8020199.

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Information about the vehicle sideslip angle is crucial for the successful implementation of advanced stability control systems. In production vehicles, sideslip angle is difficult to measure within the desired accuracy level because of high costs and other associated impracticalities. This paper presents a novel framework for estimation of the vehicle sideslip angle. The proposed algorithm utilizes an adaptive tire model in conjunction with a model-based observer. The proposed adaptive tire model is capable of coping with changes to the tire operating conditions. More specifically, extensions

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9

Zhang, Chuanwei, Yansong Feng, Jianlong Wang, Peng Gao, and Peilin Qin. "Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm." Actuators 12, no. 10 (2023): 371. http://dx.doi.org/10.3390/act12100371.

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Most existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the side slip angle of vehicles directly. Therefore, the estimation of sideslip angle has been extensively discussed in the relevant literature. Accurate modeling is complicated by the fact that vehicles are highly nonlinear. This article combines a radial basis function neural network with an unscented Kalman filter to propose a new

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10

Popowski, Stanisław, and Witold Dąbrowski. "MEASUREMENT AND ESTIMATION OF THE ANGLE OF ATTACK AND THE ANGLE OF SIDESLIP." Aviation 19, no. 1 (2015): 19–24. http://dx.doi.org/10.3846/16487788.2015.1015293.

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The paper presents issues concerning the estimation of the angle of attack and the angle of sideslip on a flying object board. Angle of attack and sideslip estimation methods which are based on measurements of linear velocity components of an object with the Earth’s coordinates and on attitude angles of the object are presented. Both of these measurements originate from the inertial navigation system, and velocity measurement is obtained from the satellite navigation system. The idea of applying inertial and satellite navigation for the estimation of attack and sideslip angles is presented. Pr

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11

Li, Yafei, Yiyong Yang, Xiangyu Wang, Yongtao Zhao, and Chengbiao Wang. "Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque." Applied Sciences 13, no. 13 (2023): 7974. http://dx.doi.org/10.3390/app13137974.

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The sideslip angle is crucial for the lateral stability state and stability control of intelligent commercial vehicles. However, sensors that can be used for direct measurements are often complex, expensive, and difficult to install in commercial vehicles. To estimate the vehicle sideslip angle, a state observer derived from the extended Kalman filter (EKF) method is proposed, and the state observer is estimated based on steering torque rather than steering angle. The transfer functions between the sideslip angle–steering torque and sideslip angle–steering angle are established, respectively,

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12

Wang, Zhenpo, Jianyang Wu, Lei Zhang, and Yachao Wang. "Vehicle sideslip angle estimation for a four-wheel-independent-drive electric vehicle based on a hybrid estimator and a moving polynomial Kalman smoother." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 233, no. 1 (2018): 125–40. http://dx.doi.org/10.1177/1464419318770923.

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This paper presents a vehicle sideslip angle estimation scheme against noises and outliers in sensor measurements for a four-wheel-independent-drive electric vehicle. The proposed scheme combines a robust unscented Kalman filter estimator based on the 3-DOF vehicle dynamics model and an extended Kalman filter estimator based on the kinematic model to form a hybrid estimator through a weighting factor. The weighting factor can be dynamically adjusted in real time to optimize the overall estimation performance under different driving conditions. The main contributions of this study to the relate

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13

Hu, Jie, Feiyue Rong, Pei Zhang, and Fuwu Yan. "Sideslip Angle Estimation for Distributed Drive Electric Vehicles Based on Robust Unscented Particle Filter." Mathematics 12, no. 9 (2024): 1350. http://dx.doi.org/10.3390/math12091350.

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An accurate and reliable sideslip angle is crucial for active safety control systems and advanced driver-assistance systems (ADAS). The direct measurement method of the sideslip angle suffers from challenges of high costs and environmental sensitivity, so sideslip angle estimation has always been a significant research issue. To improve the precision and robustness of sideslip angle estimation for distributed drive electric vehicles (DDEV) in extreme maneuvering scenarios, this paper presents a novel robust unscented particle filter (RUPF) algorithm based on low-cost onboard sensors. Firstly,

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14

Huang, Yupeng, Chunjiang Bao, Jian Wu, and Yan Ma. "Estimation of Sideslip Angle Based on Extended Kalman Filter." Journal of Electrical and Computer Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5301602.

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The sideslip angle plays an extremely important role in vehicle stability control, but the sideslip angle in production car cannot be obtained from sensor directly in consideration of the cost of the sensor; it is essential to estimate the sideslip angle indirectly by means of other vehicle motion parameters; therefore, an estimation algorithm with real-time performance and accuracy is critical. Traditional estimation method based on Kalman filter algorithm is correct in vehicle linear control area; however, on low adhesion road, vehicles have obvious nonlinear characteristics. In this paper,

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15

CHEN, Hui. "Review on Vehicle Sideslip Angle Estimation." Journal of Mechanical Engineering 49, no. 24 (2013): 76. http://dx.doi.org/10.3901/jme.2013.24.076.

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16

Wang, Quanwei, Xiaobin Fan, Zipeng Huang, Shuwen He, and Mingxin Chen. "Review on vehicle sideslip angle estimation." International Journal of Vehicle Design 94, no. 3/4 (2024): 274–320. http://dx.doi.org/10.1504/ijvd.2024.137310.

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17

Novi, Tommaso, Renzo Capitani, and Claudio Annicchiarico. "An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 7 (2018): 1864–78. http://dx.doi.org/10.1177/0954407018790646.

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Vehicle dynamics stability control systems rely on the amount of so-called sideslip angle and yaw rate. As the sideslip angle can be measured directly only with very expensive sensors, its estimation has been widely studied in the literature. Because of the large non-linearities and uncertainties in the dynamics, model-based methods are not a good solution to estimate the sideslip angle. On the contrary, machine learning techniques require large datasets that cover the entire working range for a correct estimation. In this paper, we propose an integrated artificial neural network and unscented

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18

Zhang, Fengjiao, Yan Wang, Jingyu Hu, et al. "A Novel Comprehensive Scheme for Vehicle State Estimation Using Dual Extended H-Infinity Kalman Filter." Electronics 10, no. 13 (2021): 1526. http://dx.doi.org/10.3390/electronics10131526.

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The performance of vehicle active safety systems relies on accurate vehicle state information. Estimation of vehicle state based on onboard sensors has been popular in research due to technical and cost constraints. Although many experts and scholars have made a lot of research efforts for vehicle state estimation, studies that simultaneously consider the effects of noise uncertainty and model parameter perturbation have rarely been reported. In this paper, a comprehensive scheme using dual Extended H-infinity Kalman Filter (EH∞KF) is proposed to estimate vehicle speed, yaw rate, and sideslip

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19

Sun, Tao, Hao Guo, Jian-yong Cao, Ling-jiang Chai, and Yue-dong Sun. "Study on Integrated Control of Active Front Steering and Direct Yaw Moment Based on Vehicle Lateral Velocity Estimation." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/275269.

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Considering the vehicle lateral velocity is difficult to be measured at integration of chassis control in configuration of production vehicle, this study presents the vehicle lateral velocity estimation based on the extended Kalman filtering with the standard sensor information. The fuzzy control algorithm is proposed to integrate direct yaw moment control and active front steering with lateral velocity estimation. The integration controller produces direct yaw moment and front wheel angle compensation to control yaw rate and sideslip angle, which makes the actual vehicle yaw rate and sideslip

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20

Napolitano Dell’Annunziata, Guido, Marco Ruffini, Raffaele Stefanelli, Giovanni Adiletta, Gabriele Fichera, and Francesco Timpone. "Four-Wheeled Vehicle Sideslip Angle Estimation: A Machine Learning-Based Technique for Real-Time Virtual Sensor Development." Applied Sciences 14, no. 3 (2024): 1036. http://dx.doi.org/10.3390/app14031036.

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In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and stability optimization, and its correct understanding leads to the possibility of reaching greater road safety, increased efficiency, and a better driving experience for both autonomous and human-controlled vehicles. This paper aims to estimate accurately the sideslip angle of the vehicle using different neural network confi

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21

Wang, Lu, Changkui Xu, and Jianhua Cheng. "Robust Output Path-Following Control of Marine Surface Vessels with Finite-Time LOS Guidance." Journal of Marine Science and Engineering 8, no. 4 (2020): 275. http://dx.doi.org/10.3390/jmse8040275.

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This paper proposes a finite-time output feedback methodology for the path-following task of marine surface vessels. First, a horizontal path-following model is established with unknown sideslip angle, unmeasured system state and system uncertainties. A hierarchical control structure is adopted to deal with the cascade property. For kinematics system design, a finite-time sideslip angle observer is first proposed, and thus the sideslip angle estimation is compensated in a nonlinear line-of-sight (LOS) guidance strategy to acquire finite-time convergence. For the heading control design, an exte

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22

Hankovszki, Zoltán, Roland Kovács, and László Palkovics. "Electronic stability program with vehicle sideslip estimation." Periodica Polytechnica Transportation Engineering 41, no. 1 (2013): 13. http://dx.doi.org/10.3311/pptr.7094.

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23

Selmanaj, Donald, Matteo Corno, Giulio Panzani, and Sergio M. Savaresi. "Vehicle sideslip estimation: A kinematic based approach." Control Engineering Practice 67 (October 2017): 1–12. http://dx.doi.org/10.1016/j.conengprac.2017.06.013.

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24

Chen, B. C., and F. C. Hsieh. "Sideslip angle estimation using extended Kalman filter." Vehicle System Dynamics 46, sup1 (2008): 353–64. http://dx.doi.org/10.1080/00423110801958550.

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25

Ping, Xianyao, Shuo Cheng, Wei Yue, Yongchang Du, Xiangyu Wang, and Liang Li. "Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 14 (2020): 3224–38. http://dx.doi.org/10.1177/0954407020941410.

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Vehicle dynamic states and parameters, such as the tyre–road friction coefficient and body’s sideslip angle especially, are crucial for vehicle dynamics control with close-loop feedback laws. Autonomous vehicles also have strict demands on real-time knowledge of those information to make reliable decisions. With consideration of the cost saving, some estimation methods employing high-resolution vision and position devices are not for the production vehicles. Meanwhile, the bad adaptability of traditional Kalman filters to variable system structure restricts their practical applications. This p

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Chen, Jian Feng, Xiao Dong Sun, Long Chen, and Hao Bin Jiang. "Estimation of Vehicle Sideslip Angle Using Strong Tracking SRUKF." Applied Mechanics and Materials 614 (September 2014): 267–70. http://dx.doi.org/10.4028/www.scientific.net/amm.614.267.

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Sideslip angle is an important parameter for the stability control of high-speed vehicles. In this paper, a novel state observer based on strong tracking SRUKF is presented to estimate the sideslip angle. Besides the strong tracking SRUKF algorithm, a 2-DOF vehicle model and a “Magic Formula” are utilized to construct the state observer. Numerical simulations are implemented to testify on the accuracy performance of estimation based on the strong tracking SRUKF and standard UKF. The results show that the trends using two types of filters are accordant with the theoretic values, and the accurac

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Li, Fu, and Zhen Luo. "Effects of Pressure Ports Layout on Angle of Attack and Sideslip Estimation in the Flush Air Data System." Advanced Materials Research 383-390 (November 2011): 2996–3000. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2996.

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Flush air data system (FADS) have been successfully used on the nose tip of large manned/unmanned air vehicles instead of a traditional noseboom air data system. In order to integrate FADS with strapdown inertial navigation, high accuracy of angle of attack and sideslip is required. The estimated accuracy of angle of attack and sideslip in three types of FADS, which have different pressure ports layout, is compared using the nonlinear least squares theory. Evaluation function is provided to evaluate the accuracy of angle of attack and sideslip in different pressure ports layout. The results sh

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28

Ryu, Jihan, and J. Christian Gerdes. "Integrating Inertial Sensors With Global Positioning System (GPS) for Vehicle Dynamics Control." Journal of Dynamic Systems, Measurement, and Control 126, no. 2 (2004): 243–54. http://dx.doi.org/10.1115/1.1766026.

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This paper demonstrates a method of estimating several key vehicle states—sideslip angle, longitudinal velocity, roll and grade—by combining automotive grade inertial sensors with a Global Positioning System (GPS) receiver. Kinematic Kalman filters that are independent of uncertain vehicle parameters integrate the inertial sensors with GPS to provide high update estimates of the vehicle states and the sensor biases. Using a two-antenna GPS system, the effects of pitch and roll on the measurements can be quantified and are demonstrated to be quite significant in sideslip angle estimation. Emplo

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Li, Jing, and Jiaxu Zhang. "Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3269142.

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Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF), which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF), which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation ou

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Huang, Yanwei, Xiaocheng Shi, Wenchao Huang, and Shaobin Chen. "Internal Model Control-Based Observer for the Sideslip Angle of an Unmanned Surface Vehicle." Journal of Marine Science and Engineering 10, no. 4 (2022): 470. http://dx.doi.org/10.3390/jmse10040470.

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Since the sideslip angle is often ignored or simplified in the process of path following of unmanned surface vehicle by using the line-of-sight (LOS) guidance law because of its fast change and the difficulty of measurement, an observer was proposed by internal model control (IMC) to quickly estimate the sideslip angle in the LOS guidance law. First, a prediction model was established for the tracking error, and a state space model for prediction errors was constructed as an internal model. With the introduction of the auxiliary variables, a new augmented system was set up for a state space mo

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31

Li, Xiaoyu, Nan Xu, Qin Li, Konghui Guo, and Jianfeng Zhou. "A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 7 (2019): 1930–43. http://dx.doi.org/10.1177/0954407019892156.

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This article introduces a reliable fusion methodology for vehicle sideslip angle estimation, which only needs the Controller Area Network–Bus signals of production vehicles and has good robustness to vehicle parameters, tire information, and road friction coefficient. The fusion methodology consists of two basic approaches: the kinematic-based approach and the model-based approach. The former is constructed into the extended Kalman filter for transient stage and large magnitude estimation, while the latter is designed to be an adaptive scheme for steady-state and small magnitude estimation. On

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32

Wu, Shipeng, Hui Ye, Wei Liu, Xiaofei Yang, Ziqing Liu, and Hao Zhang. "An Improved ELOS Guidance Law for Path Following of Underactuated Unmanned Surface Vehicles." Sensors 24, no. 16 (2024): 5384. http://dx.doi.org/10.3390/s24165384.

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In this paper, targeting the problem that it is difficult to deal with the time-varying sideslip angle of an underactuated unmanned surface vehicle (USV), a line–of–sight (LOS) guidance law based on an improved extended state observer (ESO) is proposed. A reduced-order ESO is introduced into the identification of the sideslip angle caused by the environmental disturbance, which ensures a fast and accurate estimation of the sideslip angle. This enables the USV to follow the reference path with high precision, despite external disturbances from wind, waves, and currents. These unknown disturbanc

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33

Selmanaj, D., M. Corno, G. Panzani, and S. M. Savaresi. "Robust Vehicle Sideslip Estimation Based on Kinematic Considerations." IFAC-PapersOnLine 50, no. 1 (2017): 14855–60. http://dx.doi.org/10.1016/j.ifacol.2017.08.2513.

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34

Chung, S., and H. Lee. "Vehicle sideslip estimation and compensation for banked road." International Journal of Automotive Technology 17, no. 1 (2016): 63–69. http://dx.doi.org/10.1007/s12239-016-0005-1.

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Schettini, Francesco, Gianpietro Di Rito, and Eugenio Denti. "Aircraft flow angles calibration via observed-based wind estimation." Aircraft Engineering and Aerospace Technology 91, no. 7 (2019): 1033–38. http://dx.doi.org/10.1108/aeat-06-2017-0145.

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Purpose This paper aims to propose a novel approach, in which the reference data for the flow angles calibration are obtained by using measurements coming from an inertial navigation system and an air data sensor. Design/methodology/approach This is obtained by using the Kalman filter theory for the evaluation of the reference angle-of-attack and angle-of-sideslip. Findings The designed Kalman filter has been implemented in Matlab/Simulink and validated using flight data coming from two very different aircraft, the Piaggio Aerospace P1HH medium altitude long endurance unmanned aerial system an

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Wang, Hengyang, Biao Liu, and Junchao Qiao. "Advanced High-Speed Lane Keeping System of Autonomous Vehicle with Sideslip Angle Estimation." Machines 10, no. 4 (2022): 257. http://dx.doi.org/10.3390/machines10040257.

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An advanced LKS (lane keeping system) for use on curving roads is presented to maintain autonomous vehicle driving within the target lane, without unintentional lane departure. There are the following two main objectives in designing this system: one is performing perfect lane keeping and the other is ensuring the dynamic stability of the vehicle, especially when driving on a curving and low-friction road with time-varying high speed. In this paper, a combined vehicle model, consisting of a lane keeping model and a vehicle lateral dynamic model, is firstly introduced. Then, a novel adaptive-we

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Sun, Wen, Zhenyuan Wang, Junnian Wang, Xiangyu Wang, and Lili Liu. "Research on a Real–Time Estimation Method of Vehicle Sideslip Angle Based on EKF." Sensors 22, no. 9 (2022): 3386. http://dx.doi.org/10.3390/s22093386.

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In this article, a real–time vehicle sideslip angle state observer is proposed, which is based on the EKF algorithm. Firstly, based on a 2–DOF dynamical model and the tire lateral force model, the vehicle sideslip angle state observer model with a self–adapted truncation procedure is established by combining the EKF and the least squares methods. The calculation of the Jacobi matrix in the time domain is transformed into a calculation in the frequency domain. A self–adapted update noise estimation method and an initial value setting strategy are proposed as well. Finally, a hardware–in–the–loo

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38

Zhang, Zhenzhao, Liang Chu, Jiaxu Zhang, Chong Guo, and Jing Li. "Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation." Applied Sciences 11, no. 3 (2021): 1231. http://dx.doi.org/10.3390/app11031231.

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This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle.

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39

Hou, SuoJun, Wenbo Xu, and Gang Liu. "Design of an Interacting Multiple Model-Cubature Kalman Filter Approach for Vehicle Sideslip Angle and Tire Forces Estimation." Mathematical Problems in Engineering 2019 (June 20, 2019): 1–13. http://dx.doi.org/10.1155/2019/6087450.

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Vehicle states estimation (e.g., vehicle sideslip angle and tire force) is a key factor for vehicle stability control. However, the accurate values of these parameters could not be obtained directly. In this paper, an interacting multiple model-cubature Kalman filter (IMM-CKF) is used to estimate the vehicle state parameters. And improvements about estimation method are achieved in this paper. Firstly, the accuracy of the reference model is improved by building two different models: one is 7-degree-of-freedom (7 DOF) vehicle model with linear tire model, and the other is 7 DOF vehicle model wi

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Atheupe, Gaël P., Younesse El Mrhasli, Ulrich Emabou, Bruno Monsuez, Kenneth Bordignon, and Adriana Tapus. "Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm." Sensors 23, no. 13 (2023): 5877. http://dx.doi.org/10.3390/s23135877.

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This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seaml

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Leanza, Antonio, Giulio Reina, and José-Luis Blanco-Claraco. "A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation." Sensors 21, no. 16 (2021): 5409. http://dx.doi.org/10.3390/s21165409.

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Sideslip angle is an important variable for understanding and monitoring vehicle dynamics, but there is currently no inexpensive method for its direct measurement. Therefore, it is typically estimated from proprioceptive sensors onboard using filtering methods from the family of the Kalman filter. As a novel alternative, this work proposes modeling the problem directly as a graphical model (factor graph), which can then be optimized using a variety of methods, such as whole-dataset batch optimization for offline processing or fixed-lag smoothing for on-line operation. Experimental results on r

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Schmeitz, A. J. C., and A. P. Teerhuis. "Robustness and Applicability of a Model-Based Tire State Estimator for an Intelligent Tire." Tire Science and Technology 46, no. 2 (2018): 105–26. http://dx.doi.org/10.2346/tire.18.460204.

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ABSTRACT Tire states can be estimated by measuring the tire contact patch shape as it varies with vertical load, longitudinal and lateral slip, and so on. In this study, a miniature triaxial accelerometer is used to measure the centripetal accelerations at the tire inner liner. A tire state estimator (TSE) algorithm is developed to transform the measured accelerations to actual tire states, in this case vertical load. The approach used for the TSE is the extended Kalman filter (EKF), but an additional peak detection algorithm is used to synchronize the simulation model with the measurement sig

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Pieniążek, Wiesław, Stanisław Wolak, and Robert Janczur. "A method for the estimation of sideslip angle for a vehicle equipped with a one-antenna GPS measuring system." Archives of Automotive Engineering – Archiwum Motoryzacji 84, no. 2 (2019): 137–46. http://dx.doi.org/10.14669/am.vol84.art10.

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One-antenna GPS systems present no possibility for the direct determination of vehicle slip angle. This is an easy task for dual antenna systems; however, many users have this kind of apparatus. In this paper, a method of estimation of this parameter, which is important for the estimation of vehicle steerability factors, is proposed (e.g. TB factor calculated on the basis of data from input test [8]). The method is based on two parameters measured by a one-antenna GPS system; these are the heading angle created from the Doppler channel coming directly from the GPS engine, and the yaw rate meas

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Liu, Jizheng, Zhenpo Wang, Lei Zhang, and Paul Walker. "Sideslip angle estimation of ground vehicles: a comparative study." IET Control Theory & Applications 14, no. 20 (2020): 3490–505. http://dx.doi.org/10.1049/iet-cta.2020.0516.

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Liu, Wei, Xin Xia, Lu Xiong, Yishi Lu, Letian Gao, and Zhuoping Yu. "Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic." IEEE Sensors Journal 21, no. 19 (2021): 21675–87. http://dx.doi.org/10.1109/jsen.2021.3059050.

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Ghosh, Jyotishman, Andrea Tonoli, Nicola Amati, and Weitao Chen. "Sideslip Angle Estimation of a Formula SAE Racing Vehicle." SAE International Journal of Passenger Cars - Mechanical Systems 9, no. 2 (2016): 944–51. http://dx.doi.org/10.4271/2016-01-1662.

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Wang, Shu, Qiang Yu, Xuan Zhao, Shuo Zhang, and Yiming Ye. "Vehicle sideslip angle estimation based on SVD-UPF algorithm." Journal of Intelligent & Fuzzy Systems 37, no. 4 (2019): 4563–73. http://dx.doi.org/10.3233/jifs-179290.

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Ma, Biao, Yahui Liu, Yefeng Gao, Yiyong Yang, Xuewu Ji, and Ying Bo. "Estimation of vehicle sideslip angle based on steering torque." International Journal of Advanced Manufacturing Technology 94, no. 9-12 (2016): 3229–37. http://dx.doi.org/10.1007/s00170-016-9426-2.

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Kim, H. H., and J. Ryu. "Sideslip angle estimation considering short-duration longitudinal velocity variation." International Journal of Automotive Technology 12, no. 4 (2011): 545–53. http://dx.doi.org/10.1007/s12239-011-0064-2.

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Zhang, Qiang, Jun Xiao, and Xiuhao Xi. "Estimation of Vehicle Longitudinal Speed Based on Improved Kalman Filter." Journal of Physics: Conference Series 2113, no. 1 (2021): 012011. http://dx.doi.org/10.1088/1742-6596/2113/1/012011.

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Abstract Estimation of vehicle longitudinal acceleration is very important in vehicle active safety control system. In this paper, two driving conditions of a 4WD off-road vehicle are divided by vehicle signals such as steering angle. Under different working conditions, different estimation algorithms are adopted. In the straight driving condition, the longitudinal speed was estimated by adjusting the variance weight of acceleration Kalman observation noise based on kinematics method. For steering conditions, in order to obtain the longitudinal velocity at the center of mass, by dynamic method

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