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
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
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
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
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, 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
Automation & Control Systems
Pan Wang, Xiaobin Fan, Xinbo Chen, Juean Yi, Shuwen He
Summary: In this study, a four-wheel motor driven electric vehicle is taken as the research object to study the estimation problem of the sideslip angle in the vehicle's nonlinear state. An Unscented Kalman Filter (UKF) estimation method is proposed to reduce observation error and improve the practicability of the estimation system. The effectiveness of the algorithm is verified by comparing with the Extended Kalman Filter (EKF) algorithm and conducting real vehicle road tests.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(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, Mechanical
Antonio Leanza, Giacomo Mantriota, Giulio Reina
Summary: Accurate knowledge of vehicle dynamics response is crucial for improving handling performance and ensuring safe driving. However, due to cost and technological limitations, not all quantities of interest can be directly measured. Model-based estimation methods, such as Kalman Filtering (KF), have been developed to map the relationship between uncertain quantities and measurable variables. This paper compares models of varying fidelity and KF-based estimators to guide the construction of a model-based observer. Nonlinear estimation algorithms, including the Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF), are contrasted with the standard Extended Kalman Filter (EKF) using experimental data from a public dataset.
VEHICLE SYSTEM DYNAMICS
(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)
Article
Engineering, Mechanical
Xin Xia, Lu Xiong, Yishi Lu, Letian Gao, Zhuoping Yu
Summary: This paper proposes a vehicle-kinematic-model-based sideslip angle estimation method which fuses information from IMU and GNSS, aligning the heading from GNSS to improve accuracy. Through various tests, the effectiveness of the method in improving sideslip angle estimation accuracy is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Chemistry, Analytical
Wenfei Li, Huiyun Li, Kun Xu, Zhejun Huang, Ke Li, Haiping Du
Summary: This paper presents a new method for estimating vehicle dynamic parameters using a two-stage estimation method including multiple-models and the Unscented Kalman Filter. Simulation results show that the proposed method is effective and can well estimate vehicle dynamic parameters.
Review
Engineering, Mechanical
Zhihuang Zhang, Jintao Zhao, Changyao Huang, Liang Li
Summary: This paper proposes an innovative vehicle kinematic-based method for estimating the sideslip angle, which utilizes multi-sensor fusion and measurement updates to improve the accuracy of estimation and meet the safety control requirements for autonomous driving.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2022)
Article
Automation & Control Systems
Xin Xia, Ehsan Hashemi, Lu Xiong, Amir Khajepour
Summary: This study proposes an autonomous vehicle sideslip angle estimation algorithm based on consensus and vehicle kinematics/dynamics synthesis. A consensus Kalman information filter is developed to enhance the observability and improve the estimation accuracy of the heading error. Experimental results demonstrate the reliability and accuracy of the estimator in various automated driving conditions.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
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)
