Article
Automation & Control Systems
Xiaolin Ding, Zhenpo Wang, Lei Zhang
Summary: This article proposes an enabling event-triggered sideslip angle estimator using a low-cost GPS and IMU, which accurately estimates the vehicle sideslip angle and velocity based on kinematic information. The proposed scheme demonstrates better estimation accuracy, reliability, and real-time performance compared to other typical estimators through hardware-in-loop and field tests.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Mechanical
Chuanwei Zhang, Yansong Feng, Jianlong Wang, Peng Gao, Peilin Qin
Summary: This study proposes a new method for estimating the sideslip angle of vehicles using a radial basis function neural network and an unscented Kalman filter. The experiment shows that this method achieves optimal results in controlling the dynamic behavior of vehicles.
Article
Chemistry, Multidisciplinary
Yafei Li, Yiyong Yang, Xiangyu Wang, Yongtao Zhao, Chengbiao Wang
Summary: This study proposes a state observer based on the EKF method to estimate the vehicle sideslip angle using steering torque instead of steering angle. Transfer functions between the sideslip angle-steering torque and sideslip angle-steering angle are established, and the analysis shows that the steering torque signal reacts more rapidly and directly due to hydraulic pressure. Finally, the proposed method is validated through a simulation hardware-in-the-loop bench test, showing accurate reflection of the sideslip angle with good reliability and effectiveness.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Rui Song, Yongchun Fang, Haoqian Huang
Summary: This article introduces a method for accurate estimation of vehicle sideslip angle and attitude angles. The method takes into account the variation of wheels cornering stiffness and addresses it by introducing a recursive least squares approach. An optimized moving horizon estimator is proposed to obtain the vehicle sideslip angle based on the nonlinear vehicle dynamic model and the investigated coupling effect between lateral and longitudinal velocity, integrating an iteration decent algorithm. Furthermore, a framework consisting of inertial navigation system measurements, a dual neural network, and a square-root cubature Kalman filter is designed to alleviate the influence of sensor noise and varied maneuvers when estimating the system states. Extensive simulation and field experiments are carried out to verify the effectiveness of the developed method in different driving scenarios, showing satisfactory estimation accuracy superior to existing methods.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Electrical & Electronic
Cuong M. Nguyen, Anh-Tu Nguyen, Sebastien Delprat
Summary: This paper presents a neural network based Takagi-Sugeno (TS) fuzzy observer for estimating the lateral speed or sideslip angle of nonlinear vehicle dynamics in the presence of modeling uncertainties and unknown inputs. A TS fuzzy reduced-order observer design is proposed for nonlinear systems, ensuring stability and robustness against modeling uncertainty using the H-infinity filtering method. A data-driven approach is introduced to construct feedforward neural networks for uncertainty approximation, which effectively mitigates the effect of uncertainty and improves estimation quality. Experimental results with the INSA autonomous vehicle demonstrate the effectiveness of the proposed TS fuzzy observer under various driving scenarios, especially in extreme conditions, using performance comparisons with a new reduced-order observer scheme incorporating NN-based uncertainty approximation.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Antonio Leanza, Giulio Reina, Jose-Luis Blanco-Claraco
Summary: The study proposed a novel approach to model vehicle dynamics directly as a graphical model, which can accurately estimate and monitor sideslip angle, with a flexible mathematical framework and greater potential for future extensions.
Article
Engineering, Multidisciplinary
Yingjie Liu, Dawei Cui
Summary: An adaptive fading unscented Kalman filter (AFUKF) algorithm was proposed for vehicle state estimation. A 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established, and a vehicle state estimator based on Kalman filter was designed. Simulation verification showed the effectiveness and reliability of the designed estimator for vehicle state estimation.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Zhenzhao Zhang, Liang Chu, Jiaxu Zhang, Chong Guo, Jing Li
Summary: This study utilizes ADUKF to estimate the sideslip angle and AFRBF-SMC to achieve vehicle stability control. The use of adaptive double-layer unscented Kalman filter and vehicle stability control algorithm is validated through simulation to ensure driving stability.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Analytical
Wen Sun, Zhenyuan Wang, Junnian Wang, Xiangyu Wang, Lili Liu
Summary: In this article, a real-time vehicle sideslip angle state observer based on the EKF algorithm is proposed. The observer model is established by combining the EKF and least squares methods, with a self-adapted truncation procedure. The calculation of the Jacobi matrix is transformed into the frequency domain, and a self-adapted update noise estimation method and an initial value setting strategy are proposed. Hardware-in-the-loop simulation is carried out to verify and analyze the real-time reliability of the estimation method using RMSE.
Article
Construction & Building Technology
Fizza Hussain, Yasir Ali, Muhammad Irfan, Murtaza Ashraf, Shafeeq Ahmed
Summary: This study proposes a data-driven model based on Convolutional Neural Network (CNN) to predict the phase angle behavior of AC mixtures, which captures 90% of the variance in the test data. The model significantly improves upon other machine learning models and linear regression, providing a surrogate to tedious laboratory testing for transport agencies and practitioners.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Engineering, Electrical & Electronic
Nan Xu, Yanjun Huang, Hassan Askari, Zepeng Tang
Summary: This study proposes an accurate estimation method for tire slip angles by combining intelligent tire technology and machine learning techniques. Experimental results show that machine learning techniques, especially in frequency domain, can accurately estimate tire slip angles up to 10 degrees. Accurate estimation of tire slip angles is crucial for advanced vehicle control.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Mechanical
Victor Mazzilli, Davide Ivone, Stefano De Pinto, Leonardo Pascali, Michele Contrino, Giulio Tarquinio, Patrick Gruber, Aldo Sorniotti
Summary: The study evaluates the performance improvement of an unscented Kalman filter using vertical and longitudinal tyre contact force signals obtained through smart tyres for vehicle speed and sideslip angle estimation. The use of smart tyre information results in a significant reduction of estimation errors and enhances the estimator's robustness and adaptability to variations in vehicle and tyre parameters.
VEHICLE SYSTEM DYNAMICS
(2022)
Article
Mechanics
Elvis Villano, Basilio Lenzo, Aleksandr Sakhnevych
Summary: A novel method for estimating vehicle sideslip angle is proposed in this paper, utilizing a combination of kinematic and dynamic approaches with cross-feedback, and validated on experimental data obtained from different race tracks. The method shows promising results in improving both sideslip angle estimation accuracy and vehicle longitudinal velocity estimation compared to current state-of-the-art techniques.
Article
Automation & Control Systems
Stefano Carnier, Matteo Corno, Sergio M. Savaresi
Summary: This paper presents a robust sideslip estimation method for unknown road grip conditions. It utilizes a hybrid kinematic-dynamic closed-loop observer with a friction classifier to adapt to rapid changes in road conditions. Experimental results show that the approach accurately estimates sideslip on different road surfaces with an error of less than 1.5 degrees.
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2023)
Article
Engineering, Mechanical
Peng Wang, Hui Pang, Zijun Xu, Jiamin Jin
Summary: This paper proposes an effective co-estimation method based on the UKF algorithm to accurately predict the sideslip angle, yaw rate, and longitudinal speed of a ground vehicle. The results show that the UKF-based vehicle driving state estimation method is effective and more accurate in different working scenarios compared with the EKF-based estimation method.
MECHANICAL SCIENCES
(2021)
