Готові списки джерел за темами / Sideslip estimation

Добірка наукової літератури з теми "Sideslip estimation"

Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Sideslip estimation"

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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Анотація:

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 accuracy in small slip angle range.

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Singh, Kanwar Bharat. "Virtual sensor for real-time estimation of the vehicle sideslip angle." Sensor Review 40, no. 2 (July 29, 2019): 255–72. http://dx.doi.org/10.1108/sr-11-2018-0300.

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Анотація:

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 estimator at par with expensive commercially available sensors used for physically measuring the vehicle sideslip angle. Design/methodology/approach The proposed estimator uses an adaptive tire model in conjunction with a model-based observer. The performance of the estimator is evaluated through experimental tests on a rear-wheel drive vehicle. Findings Detailed experimental results show that the developed system can reliably estimate the vehicle sideslip angle during both steady state and transient maneuvers, within the desired accuracy levels. Originality/value This paper presents a novel framework for vehicle sideslip angle estimation. The presented framework combines an adaptive tire model, an unscented Kalman filter-based axle force observer and data from tire mounted sensors. Tire model adaptation is achieved by making extensions to the magic formula, by accounting for variations in the tire inflation pressure, load, tread-depth and temperature. Predictions with the adapted tire model were validated by running experiments on the Flat-Trac® machine. The benefits of using an adaptive tire model for sideslip angle estimation are demonstrated through experimental tests. The performance of the observer is satisfactory, in both transient and steady state maneuvers. Future work will focus on measuring tire slip angle and road friction information using tire mounted sensors and using that information to further enhance the robustness of the vehicle sideslip angle observer.

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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 longitudinal force. Using the estimated longitudinal force, considering the unknown disturbances and uncertainties of vehicle model, the robust sideslip angle estimator is proposed based on vehicle dynamics model. Moreover, the recursive least squares algorithm with forgetting factor is applied to vehicle state estimation based on the vehicle kinematics model. In order to integrate the advantages of the dynamics-model-based observer and kinematics-model-based observer and improve adaptability of observer system in complex working conditions, a vehicle sideslip angle fusion estimation strategy is proposed. The simulations and experiments are implemented and the performance of proposed estimation method is validated.

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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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Анотація:

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. Estimation results of neural network and real vehicle experiment show the same changing tendency. The mean of error is within 10% of test result’s amplitude. Results show GRNN can estimate vehicle’s sideslip angle correctly. It can offer a reference to the application of vehicle’s stability control system on vehicle’s state estimation.

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Xia, Qiu, Long Chen, Xing Xu, Yingfeng Cai, Haobin Jiang, Te Chen, and Guangxiang Pan. "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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Анотація:

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 design, in which the adaptive high-order sliding mode observer is designed to achieve the state estimation, the analytic formula of longitudinal force is obtained by decoupling electric driving wheel model, and the longitudinal force estimator is designed by recurrence estimation method. With the designed virtual longitudinal force sensor, an adaptive attenuated Kalman filtering is proposed to estimate the vehicle running state, in which the time-varying attenuation factor is applied to weaken the past data to the current filter and the covariance of process noise and measurement noise can be adjusted adaptively. Finally, simulations and experiments are conducted and the effectiveness of proposed estimation method is validated.

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Singh, Kanwar Bharat. "Vehicle Sideslip Angle Estimation Based on Tire Model Adaptation." Electronics 8, no. 2 (February 9, 2019): 199. http://dx.doi.org/10.3390/electronics8020199.

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Анотація:

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 have been made to Pacejka's Magic Formula expressions for the tire cornering stiffness and peak grip level. These model extensions account for variations in the tire inflation pressure, load, tread depth and temperature. The vehicle sideslip estimation algorithm is evaluated through experimental tests done on a rear wheel drive (RWD) vehicle. Detailed experimental results show that the developed system can reliably estimate the vehicle sideslip angle during both steady state and transient maneuvers.

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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 (May 30, 2019): 34. http://dx.doi.org/10.3390/wevj10020034.

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Анотація:

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 vehicle model. An adaptive measurement-update solution of the robust cubature Kalman filter is presented to improve the robustness of estimation, and then, the wheel-speed coupling relationship is introduced to the measurement update equation of the robust cubature Kalman filter and an adaptive sideslip angle fusion estimation method is designed. The simulations in the CarSim-Simulink co-simulation platform and the actual vehicle road test are carried out, and the effectiveness of the proposed estimation method is validated by corresponding comparative analysis results.

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Popowski, Stanisław, and Witold Dąbrowski. "MEASUREMENT AND ESTIMATION OF THE ANGLE OF ATTACK AND THE ANGLE OF SIDESLIP." Aviation 19, no. 1 (March 30, 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. Practical comparison of these estimation methods has been conducted based on logged parameters of a flight onboard a Mewa aircraft. Development proposals for these methods are presented as well.

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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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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 (April 24, 2018): 125–40. http://dx.doi.org/10.1177/1464419318770923.

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Анотація:

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 related literature lie in two aspects. Firstly, a robust unscented Kalman filter estimator was incorporated to improve the robustness of dynamics-based estimation to sensor measurement outliers. Secondly, a novel moving polynomial Kalman smoother was included to filter out the noises in sensor measurements. Co-simulations of Matlab/Simulink and Carsim software were conducted under typical vehicle maneuvers and show that the proposed vehicle sideslip angle estimation scheme can obtain satisfied estimation results, with the moving polynomial Kalman smoother exhibiting better phase characteristics and filtering performance relative to commonly-used finite impulse response filters, and the robust unscented Kalman filter estimator being robust to sensor measurement outliers.

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Більше джерел

Дисертації з теми "Sideslip estimation"

Alatorre, Vazquez Angel Gabriel. "Robust estimation of dynamics behavior and driving diagnosis applied to an intelligent MAGV." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2554.

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Cette thèse présente une série de stratégies pour estimer la dynamique des véhicules. Le but de ce travail est de développer une stratégie d'observation qui peut être appliquée aux véhicules de série. L'idée d'avoir un algorithme dans une voiture produite en série pose un grand nombre de défis,' ceux que nous considérons dans ce travail sont la robustesse et le coût. Nous avons proposé des modèles et des stratégies d'observateurs capables de faire face à des niveaux d'excitation élevés et faibles. Nous avons validé la robustesse de l'algorithme avec de nombreux tests, des petites pistes à faible vitesse aux manœuvres de changement de voie. Nos algorithmes ont été retravaillés plusieurs fois pour atteindre un degré de précision qui peut être utile pour l'intégration dans ADAS. Nous avons également proposé des stratégies d'observateurs qui permettent ce degré de robustesse tout en conservant une grille de capteurs à faible coût. Les principales contributions sont au nombre de trois : 1 - Estimation de la vitesse latérale et longitudinale,' ces variables sont essentielles pour une bonne rétroaction des régulateurs de stabilisation et de vitesse de croisière. Notre proposition utilise comme base un modèle cinématique pour éviter d'utiliser des paramètres liés à la masse dans notre modèle; cela est possible puisque notre grille de capteurs comprend des accéléromètres et des gyroscopes. L'une des principales contributions de cette section est la compensation de la gravité,' une équation différentielle de quaternions définit l'attitude de notre système. Plus de 100 tests valident la robustesse de l'algorithme, et nous obtenons des résultats cohérents dans chacun d'eux. 2- Estimation de l'estimation de la force pneu-sol normale. Cette variable est, à notre avis, la plus difficile à estimer car la grille de capteurs des véhicules de série ne contient pas beaucoup de capteurs mesurant la dynamique verticale. Cette section doit étendre notre solution aux véhicules de série avec des systèmes de suspension améliorés, y compris des capteurs de déflexion. Nous pouvons estimer la masse, la distribution de masse et le centre de gravité avec ces capteurs en place et transmettre l'estimation normale de la force pneu-sol en utilisant la fusion de modèles et le filtre de Kalman. 3 - Stratégies d'estimation des forces longitudinales et latérales pneu-sol. La première méthode utilise les modèles bicycle et hoverboard connus et les filtres de Kalman pour estimer les TGFs, et d'autres modèles sont introduits pour répartir ces forces sur le pneu adéquat. Cette méthode doit gérer la saturation des pneus, pour séparer correctement les TGF virtuels. La deuxième méthode utilise les lois de Newton du mouvement; ici, nous calculons les accélérations locales en utilisant l'accélération et les rotations d'un corps rigide. Étant donné que nous connaissons déjà les TGF normaux à chaque pneu, nous pouvons calculer les TGFs latéraux et longitudinaux avec précision. Cette dernière méthode est plus précise et robuste que la première méthode. Enfin, au final, nous proposons une série de systèmes qui bénéficieront des estimations antérieures
The context of this thesis is the improvement of road safety through the development of active safety systems. One challenge in the development of active safety systems is obtaining accurate information about unmeasurable vehicle dynamic states. Specifically, the necessity to estimate the vertical load, frictional forces at each wheel (longitudinal and lateral), and also the sideslip angle at the center of gravity. These states are the critical parameters for optimizing the control of a vehicle’s stability. If the vertical load on each tire can be estimated, then the risk of rollover can be evaluated. Estimating tire lateral forces can help to reduce lateral slip and prevent dangerous situations like spinning and drifting out the road. Tire longitudinal forces influence the performance of a vehicle. Sideslip angle is one of the essential parameters for controlling the lateral dynamics of a vehicle. However, the different technologies that the market offers, are not based on tire-ground forces due to the lack of cost-effective methods for obtaining the required information. For the above mentioned reasons, we want to develop a system that monitors these dynamic vehicle states using only low-cost sensors. To accomplish our endeavor, we propose developing novel observers to estimate unmeasured states. Constructing an observer that met the reliability, robustness and accuracy requirements is not an easy task. It requires one the one hand, accurate and efficient models, and on the other hand, robust estimation algorithms that take into account variations in parameters and measurement errors. The present thesis has consequently been structured around the following two aspects: modeling of vehicle dynamics, and design of observers. Under the heading of modeling, we propose new models to describe vehicle dynamics. Current models simplify the vehicle motion as a planar motion. In our proposal, our models describe vehicle motion as a 3D motion, including the effects of road inclination. Regarding vertical dynamics, we propose incorporating the suspension deflection to calculate the transfer of vertical load. Regarding lateral dynamics, we propose a model for the lateral forces transfer to describe the interaction between the left wheel and the right wheel. With this relationship, the lateral force on each tire is computed without using the sideslip angle. Similarly, for longitudinal dynamics, we also propose a model for the transfer of longitudinal forces to calculate the longitudinal force at each tire. Under the heading of observer design, we propose a novel observation system consisting of four individual observers connected in cascade. The four observers are developed for estimating vertical tire force, lateral tire force, longitudinal tire force, and sideslip angle, respectively. For the linear system, the Kalman filter is employed, while for the nonlinear system, the EKF applied to reduce estimation errors. Finally, we implement our algorithm in an experimental vehicle to perform estimation in real-time, and we validate our proposed algorithm using experimental data

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Baffet, Guillaume. "Développement et validation expérimentale d’observateurs des forces du contact pneumatique/chaussée d’une automobile." Compiègne, 2007. http://www.theses.fr/2007COMP1695.

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Анотація:

La connaissance de la dynamique d'un véhicule et de son environnement est un enjeu majeur. L'objectif de cette thèse est de développer des observateurs d'état permettant d'estimer des variables liées à l'adhérence du contact pneumatique/chaussée. Différentes méthodes d'observation sont proposées afin de reconstruire les efforts des pneumatiques et l'angle de dérive d'une automobile. Ces algorithmes d'estimation sont conçus pour être fonctionnels en cas de situation de conduite critique, notamment lors de fortes accélérations latérales ou lorsque l'adhérence de la chaussée varie. Outre une description des techniques d'observation mises en oeuvre, cette étude présente d'importantes phases de validation par la simulation et l'expérimentation. Ce travail a abouti à la conception d'un module temps réel embarqué qui a pu être évalué relativement à des mesures des forces des roues
Estimation of vehicle-dynamic variables is essential for safety enhancement, in particular for braking and trajectory-control systems. The aim of this thesis is to develop state observers for the estimation of variables linked to tire-road friction. Different estimation methods are proposed in order to reconstruct tire-road forces and vehicle sideslip angle. The estimation algorithms are constructed so as to be functional in critical driving situations, notably for weak lateral accelerations and road friction changes. In addition to estimation methods, this thesis presents a substantial number of observer evaluations, performed in simulations and in experiments. The estimation process was integrated in an experimental vehicle, and was tested in real time, particularly in relation to wheel force measurements

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Частини книг з теми "Sideslip estimation"

Kwon, Baek-soon, and Kyongsu Yi. "Vehicle Sideslip Angle Estimation Using Disturbance Observer." In Lecture Notes in Mechanical Engineering, 1584–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38077-9_181.

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Antunes, André, Carlos Cardeira, and Paulo Oliveira. "Application of Sideslip Estimation Architecture to a Formula Student Prototype." In ROBOT 2017: Third Iberian Robotics Conference, 409–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70836-2_34.

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Yang, Guibing, Chunguang Liu, and Dingzhe Qin. "Estimation of Electric Drive Vehicle Sideslip Angle Based on EKF." In Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation, 695–702. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49367-0_67.

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Pieralice, Cristiano, Basilio Lenzo, Francesco Bucchi, and Marco Gabiccini. "Vehicle Sideslip Angle Estimation Using Kalman Filters: Modelling and Validation." In Mechanisms and Machine Science, 114–22. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03320-0_12.

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Cheli, F., D. Ivone, and E. Sabbioni. "Smart Tyre Induced Benefits in Sideslip Angle and Friction Coefficient Estimation." In Sensors and Instrumentation, Volume 5, 73–83. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15212-7_9.

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Gai, Jiangtao, Yue Ma, Xuzhao Hou, Gen Zeng, and Shumin Ruan. "Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle." In Lecture Notes in Electrical Engineering, 576–83. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6226-4_57.

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Lenzo, Basilio, and Ricardo De Castro. "Vehicle Sideslip Estimation for Four-Wheel-Steering Vehicles Using a Particle Filter." In Lecture Notes in Mechanical Engineering, 1624–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38077-9_185.

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Milanese, Mario, C. Novara, and I. Gerlero. "Robust estimation of vehicle sideslip angle from variables measured by ESC system." In Proceedings, 1063–76. Wiesbaden: Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-08844-6_72.

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Xu, Fan, Hui Chen, Xiang Wang, and Junxi Xiong. "Estimation of Sideslip Angle with Tire-Road Friction Adaptation Using Nonlinear Observability Theory." In Proceedings of China SAE Congress 2020: Selected Papers, 627–53. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-2090-4_38.

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Ling, Jie, Hui Chen, and Fan Xu. "Estimation of Vehicle Sideslip Angle with Adaptation to Road Bank Angle and Roll Angle." In Lecture Notes in Electrical Engineering, 403–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45043-7_41.

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Тези доповідей конференцій з теми "Sideslip estimation"

Huang, Jihua. "Vehicle State Estimation for Rollover Avoidance." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66730.

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Анотація:

To enhance vehicle/road safety, rollover warning and control systems have received considerable research interest in recent years, especially for vehicles with high center of gravity (CG). Accurate and reliable estimates of the relevant vehicle states facilitate the design of such systems. This paper investigates the state estimation for rollover avoidance, in which the relevant states include vehicle roll velocity and roll angle, as well as sideslip velocity and yaw velocity. The main challenge of the design comes from the fact that, under near-rollover situations, vehicle dynamics is complex and nonlinear. Not only vehicle suspension and tires are in their nonlinear region, but also vehicle yaw, sideslip and roll motions are highly coupled. In addition, the estimation needs to deal with sensor biases and sensor nonlinearity under this extreme condition. To address those issues, this paper proposes a vehicle state estimation design that consists of three parts: a sensor pre-filter, an Extended Kalman filter (EKF), and a sideslip velocity estimator. The sensor pre-processor removes sensor biases by utilizing the Recursive Least Square technique with a varying forgetting factor. The EKF is designed based on a linear yaw/sideslip/roll model, and its feedback gains are further scheduled based on vehicle lateral acceleration in order to reduce the effects of increased model inaccuracy as vehicle roll motion becomes more severe. The sideslip velocity estimator adjusts the sideslip velocity estimated by the EKF to extend the estimation to the nonlinear region. Both simulation and vehicle fishhook testing have been used to verify the effectiveness of the design.

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Zha, Jingqiang, Junmin Wang, Min Li, Xin Zhang, and Xiao Yu. "Structured Robust Linear Parameter-Varying Vehicle Sideslip Angle Estimation." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9021.

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Abstract Non-smooth structured robust controller design has drawn a lot of attention recently due to its ability to deal with uncertainty and its convenience for implementation. In this paper, the method is extended to design the structured robust linear parameter-varying (LPV) estimator by pulling out scheduling variables from estimator using linear fractional transformation (LFT). The structured robust LPV estimator is then applied to vehicle sideslip angle estimation. Both the measured vehicle speed and estimated tire cornering stiffness are treated as scheduling variables to further reduce sideslip angle estimation error. The effects of estimator order and number of repetitiveness of scheduling variables are studied using a MATLAB/Simulink bicycle model. The developed approach is later verified in Hardware-in-the-Loop (HIL) simulation environment using dSPACE SCALEXIO and MicroAutoBox. A comprehensive high-fidelity dSPACE automotive simulation models (ASM) vehicle model is used for the real-time HIL simulation. Double-lane change and sine steer maneuvers have been implemented to verify the effectiveness of the structured robust LPV sideslip angle estimation method.

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Huang, Xiaoyu, and Junmin Wang. "Robust Sideslip Angle Estimation for Lightweight Vehicles Using Smooth Variable Structure Filter." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3775.

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In the design of vehicle stability control (VSC) systems for ground vehicles, sideslip angle plays a vital role and its estimation has long been an active research topic. Accurate estimation of sideslip angle is more difficult for lightweight vehicles (LWVs) because their parameters are prone to significant changes with loading conditions — the amount and position of the payload. In this paper, a robust sideslip angle estimator based on a recently emerging smooth variable structure filter (SVSF) is presented. This sideslip angle estimator is suitable for LWVs because it is almost non-sensitive to the changes of the system parameters. A four-state vehicle lateral dynamic model including a pseudo-Burckhardt tire model is employed in the filter design. Compared with the widely utilized extended Kalman filter (EKF), the SVSF shows much better robustness against modeling errors. It is also more favorable in terms of tuning effort and computational speed. Simulation studies were conducted based on a high-fidelity vehicle model in CarSim®, where the vehicle took the form of a lightweight electric ground vehicle with independent in-wheel motors. The performance of the SVSF was shown by comparisons against the EKF under different settings for model parameters.

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Botha, Theunis R., and Pieter S. Els. "Vehicle Sideslip Estimation Using Unscented Kalman Filter, AHRS and GPS." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70875.

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A vehicle’s sideslip angle is an important parameter for both vehicle control and tyre property estimation. This paper details the method of determining a vehicles sideslip angle using an Attitude Heading Reference System (AHRS) and a Global Position System (GPS) in conjunction with the Unscented Kalman Filter (UKF). The addition of a single GPS antenna and the AHRS provides the ability to directly estimate the sideslip angle. Incorporating this direct measurement, as well as the summation of the gravity and gyro-compensated lateral acceleration to provide lateral velocity, allows the continuous and drift free estimation of the sideslip angle. The method is evaluated in simulation, using a validated non-linear full vehicle ADAMS model with added sensor noise. The estimated sideslip angle compares well against the simulated vehicle’s sideslip angle.

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Liu, Wei, Lu Xiong, Xin Xia, and Zhuoping Yu. "Vehicle Sideslip Angle Estimation: A Review." In WCX World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2018. http://dx.doi.org/10.4271/2018-01-0569.

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Carlos Daniel Reyes Bautista, Pablo Siqueira Meirelles, and Olmer Garcia Bedoya. "SIDESLIP ANGLE ESTIMATION FOR GROUND VEHICLES." In 23rd ABCM International Congress of Mechanical Engineering. Rio de Janeiro, Brazil: ABCM Brazilian Society of Mechanical Sciences and Engineering, 2015. http://dx.doi.org/10.20906/cps/cob-2015-2715.

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Ryu, Jihan, Flavio Nardi, and Nikolai Moshchuk. "Vehicle Sideslip Angle Estimation and Experimental Validation." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64466.

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This paper presents a comparison study of three different methods for the estimation of vehicle’s lateral velocity: one based on algebraic relationships, a second based on a dynamic observer, and lastly one based on kinematic equations. All methods are based on the implementation of an experimental nonlinear model that relates the front and rear axles’ lateral forces to their respective side slip angles. These nonlinear models include the effects of different surface coefficients of friction, and all three methods include a real time estimation of the surface coefficient of friction based on standard production sensors. Real time estimation performance of the proposed observers is compared and evaluated based on experimental implementation on a series production vehicle. Vehicle dynamic tests include highly dynamic maneuvers such as slaloms, single and double lane changes, fishhooks on test surfaces such as ice, snow, gravel, and wet/dry asphalt.

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Panzani, Giulio, Matteo Corno, Mara Tanelli, Sergio M. Savaresi, Andrea Fortina, and Sebastiano Campo. "Control-Oriented Vehicle Attitude Estimation With Online Sensors Bias Compensation." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2531.

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In advanced vehicle stability control systems, the availability of online estimates of the vehicle attitude is essential. In four-wheeled vehicles, attitude information is deeply linked to vehicle sideslip angle and sideslip rate, since these variables are strictly related to instability phenomena, safety and handling performances. As direct measurements of such quantities cannot be performed with standard sensors equipment, the design of robust and efficient estimators is needed. In this paper, we tackle the problem by proposing a sideslip rate observer and by demonstrating how an existing sideslip estimation algorithm can be made robust and reliable by online compensation of the sensors bias via a recursive identification approach. The proposed method does not require any additional sensor, making the final solution suitable for industrial applications. Experimental results confirm the effectiveness of the sensors offset compensation and witness satisfactory results of the overall vehicle attitude estimation scheme.

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Kunnappillil Madhusudhanan, Anil, Matteo Corno, and Edward Holweg. "Vehicle sideslip estimation using tyre force measurements." In 2015 23th Mediterranean Conference on Control and Automation (MED). IEEE, 2015. http://dx.doi.org/10.1109/med.2015.7158734.

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Yoon, Jong-Hwa, and Huei Peng. "Vehicle Sideslip Angle Estimation Using Two Single-Antenna GPS Receivers." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4249.

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Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Control (ESC). Vehicle sideslip angle can be measured through optical speed sensors, or dual-antenna GPS. These measurement systems are costly (∼$5k to $100k), which prohibits wide adoption of such systems. This paper demonstrates that the vehicle sideslip angle can be estimated in real-time by using two low-cost single-antenna GPS receivers. Fast sampled signals from an Inertial Measurement Unit (IMU) compensate for the slow update rate of the GPS receivers through an Extended Kalman Filter (EKF). Bias errors of the IMU measurements are estimated through an EKF to improve the sideslip estimation accuracy. A key challenge of the proposed method lies in the synchronization of the two GPS receivers, which is achieved through an extrapolated update method. Analysis reveals that the estimation accuracy of the proposed method relies mainly on vehicle yaw rate and longitudinal velocity. Experimental results confirm the feasibility of the proposed method.

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