Статті в журналах: "Sideslip estimation"

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 accuracy in small slip angle range.

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2

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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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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3

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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4

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. 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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5

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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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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6

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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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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7

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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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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8

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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9

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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10

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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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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11

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, extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change. To test and verify the effect of extended Kalman filtering estimation algorithm, the real vehicle test was carried out on the limit test field. The experimental results show that the accuracy of vehicle sideslip angle acquired by extended Kalman filtering algorithm is obviously higher than that acquired by Kalman filtering in the area of the nonlinearity.

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12

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 (August 2, 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 Kalman filter observer using only inertial measurement unit measurements, which can work as a standalone sensor. The artificial neural network is trained solely with numerical data obtained with a Vi-Grade model and outputs a pseudo-sideslip angle which is used as input for the unscented Kalman filter. This is based on a kinematic model making the filter completely transparent to model uncertainty. A direct integration with integral damping and integral reset value allows the estimation of the longitudinal velocity of the kinematic model. A modification strategy of the pseudo-sideslip angle is then proposed to improve the convergence of the filter’s output. The algorithm is tested on both numerical data and experimental data. The results show the effectiveness of the solution.

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13

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 angle follow desirable yaw rate and desirable sideslip angle. The simulation results show vehicle handling and stability are enhanced under different driving cycles by the proposed algorithm.

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14

Zhang, Fengjiao, Yan Wang, Jingyu Hu, Guodong Yin, Song Chen, Hongdang Zhang, and Dong Zhou. "A Novel Comprehensive Scheme for Vehicle State Estimation Using Dual Extended H-Infinity Kalman Filter." Electronics 10, no. 13 (June 24, 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 angle. A three-degree-of-freedom vehicle dynamics model is first established. Based on the model, the first EH∞KF estimator is used to identify the mass of the vehicle. Simultaneously, the second EH∞KF estimator uses the result of the first estimator to predict the vehicle speed, yaw rate, and sideslip angle. Finally, simulation tests are carried out to demonstrate the effectiveness of the proposed method. The test results indicate that the proposed method has higher estimation accuracy than the extended Kalman filter.

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15

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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16

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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17

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

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18

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 (April 11, 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 extended state observer is introduced for the unmeasured state and equivalent disturbance estimation, based on which an output feedback backstepping approach is proposed for the desired tracking of command course angle. The global stability of the cascade system is analyzed. Simulation results validate the effectiveness of the proposed methodology.

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19

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 (July 30, 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 paper introduces a cost-efficient estimation scheme using on-board sensors. Improved Strong Tracking Unscented Kalman filter is constructed to estimate the friction coefficient with fast convergence rate on time-variant road surfaces. On the basis of previous step, an estimator based on interactive multiple model is built to tolerant biased noise covariance matrices and observe body’s sideslip angle. After the vehicle modelling errors are considered, a Self-Correction Data Fusion algorithm is developed to integrate results of the estimator and direct integral method with error correction theory. Some simulations and experiments are also implemented, and their results verify the high accuracy and good robustness of the cooperative estimation scheme.

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20

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 accuracy of the former is better than the latter.

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21

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 show that more sensors and radiation-type can provide high accuracy.

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22

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 outputs of two filters are merged by interacting multiple model (IMM) approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.

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23

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

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24

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

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25

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 (June 1, 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. Employing the same GPS system as an input to the estimator, this paper develops a method that compensates for roll and pitch effects to improve the accuracy of the vehicle state and sensor bias estimates. In addition, calibration procedures for the sensitivity and cross-coupling of inertial sensors are provided to further reduce measurement error. The resulting state estimates compare well to the results from calibrated models and Kalman filter predictions and are clean enough to use in vehicle dynamics control systems without additional filtering.

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26

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 (March 26, 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 model of the prediction errors. Then, the sideslip angle observer was designed by the theory of state feedback with the feature of the control law of a proportional-integral type. Theoretically, the stability of the system was proved based on the Lyapunov criteria. A simulation and experiment verified the effectiveness of the proposed sideslip angle observer in improving the path-following accuracy. The results show that the IMC-based observer introduces a proportional term of tracking error that is not considered by other observers, which is easier to implement and adjust, and has a faster response speed and a smaller steady-state error for the sideslip angle. In addition, the assumption of a small sideslip angle is not introduced in the design process, so the proposed observer provides an accurate estimation method for a large sideslip angle.

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27

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 (July 8, 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 and the Alenia-Aermacchi M346 Master™ transonic trainer. This paper illustrates some results where the filter satisfactory behaviour is verified by comparing the filter outputs with the data coming from high-accuracy nose-boom vanes. Practical implications The methodology aims to lower the calibration costs of the air data systems of an advanced aircraft. Originality/value The calibration of air-data systems for the evaluation of the flow angles is based on the availability of high-accuracy reference measurements of angle-of-attack and angle-of-sideslip. Typically, these are obtained by auxiliary sensors directly providing the reference angles (e.g. nose-boom vanes). The proposed methodology evaluates the reference angle-of-attack and angle-of-sideslip by analytically reconstructing them using calibrated airspeed measurements and inertial data.

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28

Wang, Hengyang, Biao Liu, and Junchao Qiao. "Advanced High-Speed Lane Keeping System of Autonomous Vehicle with Sideslip Angle Estimation." Machines 10, no. 4 (April 2, 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-weight predictive controller is used to calculate the desired steering angle and the additional yaw moment which provide coordinated control forlane keeping and dynamic stability control. Meanwhile, a square-root cubature Kalman filter-based vehicle sideslip angle observer, with a strong tracking theory modification (ST-SRCKF), is established to estimate the sideslip angle during the driving process. Finally, HIL (hardware-in-the-loop) tests and field tests are constructed, and the results show the effectiveness of our proposed LKS controller and ST-SRCKF sideslip angle estimation.

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29

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 (December 24, 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 this basis, combining the advantages of the two methods, a weight allocation strategy is proposed based on the front wheel steering angle and transient characteristics of lateral acceleration and yaw rate. The validity of the method is verified by simulation and experiment, and it is proved that the method can be effectively used for the sideslip angle estimation.

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30

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 (January 29, 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. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.

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31

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 (April 28, 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–loop simulation is carried out by Matlab/Simulink–CarSim–dSPACE, and the real–time reliability of the estimation method is verified and analyzed by RMSE.

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32

Leanza, Antonio, Giulio Reina, and José-Luis Blanco-Claraco. "A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation." Sensors 21, no. 16 (August 10, 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 real vehicle datasets validate the proposal, demonstrating a good agreement between estimated and actual sideslip angle, showing similar performance to state-of-the-art methods but with a greater potential for future extensions due to the more flexible mathematical framework. An open-source implementation of the proposed framework has been made available online.

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33

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 (December 2020): 3490–505. http://dx.doi.org/10.1049/iet-cta.2020.0516.

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34

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 (October 1, 2021): 21675–87. http://dx.doi.org/10.1109/jsen.2021.3059050.

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35

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 (April 5, 2016): 944–51. http://dx.doi.org/10.4271/2016-01-1662.

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36

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 (October 25, 2019): 4563–73. http://dx.doi.org/10.3233/jifs-179290.

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37

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 (September 17, 2016): 3229–37. http://dx.doi.org/10.1007/s00170-016-9426-2.

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38

Kim, H. H., and J. Ryu. "Sideslip angle estimation considering short-duration longitudinal velocity variation." International Journal of Automotive Technology 12, no. 4 (July 7, 2011): 545–53. http://dx.doi.org/10.1007/s12239-011-0064-2.

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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 with nonlinear Dugoff tire model. Secondly, the different models are switched by IMM-CKF to match different driving condition. Thirdly, the lateral acceleration correction for sideslip angle estimation is considered, because the sensor of lateral acceleration is easy to be influenced by the gravity on banked road. Then, to compare cubature Kalman filter (CKF) estimation method and IMM-CKF estimation method Hardware-In-Loop (HIL) tests are carried out in the paper. And simulation results show that IMM-CKF methodology can provide accurate estimation values of vehicle states parameters.

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40

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 (April 1, 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 signal before applying the EKF. The simulation model used in the EKF is an empirical model that describes the basic shape of the centripetal acceleration signal. The applicability of the estimator is assessed by considering the accuracy and robustness for several tire operating conditions: vertical load, velocity, inflation pressure, sideslip, camber, and braking. It is concluded that the TSE exhibits accurate vertical load estimation even in cases of varying load and velocity. Further, it is concluded that the vertical load estimation is robust for (pure) camber changes and (pure) longitudinal force disturbances. For relatively high lateral forces as result of sideslip, the estimation error is larger. The current estimator appears to be not robust for inflation pressure changes, but this can be solved by adding an inflation pressure sensor. Similarly, extension of the estimator to estimate lateral force by adding a second accelerometer not only provides an additional state but also adds the possibility of improving the vertical load estimation. Finally, it is demonstrated that the TSE is able to perform in real time and shows fast convergence capabilities for cases in which the initial vertical load and/or sensor position are unknown or when moving away from situations in which the signal-to-noise ratio is poor.

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41

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 (June 28, 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 measured by an IMU device integrated and cooperating with the GPS engine. The sideslip angle which was calculated according to the proposed method is compared with an equivalent angle calculated on the basis of data from a non-slip measurement of velocity components for selected point of vehicle acquired using. The presented method is illustrated with examples from real tests. In the author’s opinion, the sideslip angle calculated with the application of measurement data obtained from a one-antenna GPS device could be used in practice. From comparison with another upper mentioned method, it follows that the differences between average values of sideslip angles obtained from both considered methods is not greater than 8%.

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42

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 (November 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, a lateral state estimator was designed and tire sideslip dynamics was modeled. The CarSim-Simulink co-simulation results show that the proposed algorithm has high accuracy and strong practicability.

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43

Yu, Zhuoping, Xin Xia, Lu Xiong, Letian Gao, and Yishi Lu. "Vehicle sideslip angle estimation: fusion of vehicle kinematics and dynamics." International Journal of Vehicle Design 87, no. 1/2/3/4 (2021): 73. http://dx.doi.org/10.1504/ijvd.2021.10047016.

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44

Li, Xu, Xiang Song, and Chingyao Chan. "Reliable vehicle sideslip angle fusion estimation using low-cost sensors." Measurement 51 (May 2014): 241–58. http://dx.doi.org/10.1016/j.measurement.2014.02.007.

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45

Baffet, Guillaume, Ali Charara, and Daniel Lechner. "Estimation of vehicle sideslip, tire force and wheel cornering stiffness." Control Engineering Practice 17, no. 11 (November 2009): 1255–64. http://dx.doi.org/10.1016/j.conengprac.2009.05.005.

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46

Fergani, S., C. Jauberthie, and L. Travé-Massuyès. "Automotive vehicle sideslip angles estimation in a bounded-error context." IFAC-PapersOnLine 50, no. 1 (July 2017): 14830–35. http://dx.doi.org/10.1016/j.ifacol.2017.08.2582.

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47

Miao, Zhibin, Hongtian Zhang, and Jinzhu Zhang. "A Robust Method of Vehicle Stability Accurate Measurement Using GPS and INS." Measurement Science Review 15, no. 6 (December 1, 2015): 294–303. http://dx.doi.org/10.1515/msr-2015-0040.

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Abstract With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

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48

Singh, Jatinder, and S. C. Raisinghani. "Aileron and sideslip-induced unsteady aerodynamic modeling for lateral parameter estimation." Journal of Aircraft 30, no. 4 (July 1993): 512–18. http://dx.doi.org/10.2514/3.46373.

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49

Khatir, TABTI. "Electric vehicle yaw moment control based on the body sideslip estimation." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 11 (November 2, 2021): 99–103. http://dx.doi.org/10.15199/48.2021.11.17.

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50

Cheng, Qi, Alessandro Correa Victorino, and Ali Charara. "Nonlinear observer of sideslip angle using a particle filter estimation methodology." IFAC Proceedings Volumes 44, no. 1 (January 2011): 6266–71. http://dx.doi.org/10.3182/20110828-6-it-1002.01162.

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