Vehicle state and parameter estimation based on adaptive robust unscented particle filter

Liu, Yingjie; Cui, Dawei; Peng, Wen

doi:10.21595/jve.2022.22788

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Wang et al 19 proposed an adaptive robust UKF based on the Sage-Husa filter and the robust estimation technique for AUV acoustic navigation. Liu et al 20 proposed a filter estimation method of vehicle state based on the principle of robust filtering and the unscented particle filter algorithm in order to solve the problem that the measured values of key state parameters such as the lateral velocity and yaw rate of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Vehicle dynamics prediction via adaptive robust unscented particle filter

Liu

Cui

2023

Advances in Mechanical Engineering

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Accurate knowledge of the vehicle dynamics response is a critical aspect to improve handling performance while ensuring safe driving at the same time. However, it poses a challenge since not all the quantities of interest can be directly measured due to cost and/or technological reasons. Therefore, combining the principle of robust filtering and unscented particle filtering algorithm, a filter estimation method of vehicle state is proposed to estimate driving state parameters of a vehicle. The adaptive robust unscented particle filter (ARUPF) is used to realize the longitudinal and lateral velocity as well as the side slip angle of the vehicle. The CarSim and Matlab/Simulink co-simulation platform is established to verify the estimation algorithm. The results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving states can be estimated, the measurement parameters can be effectively filtered, and the estimation accuracy is higher.

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Section: Introductionmentioning

confidence: 99%

Vehicle dynamics prediction via adaptive robust unscented particle filter

Liu

Cui

2023

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

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Research on Vehicle Stability Control Based on a Union Disturbance Observer and Improved Adaptive Unscented Kalman Filter

Li,

Feng,

Zhang

et al. 2024

Electronics

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This paper considers external disturbances imposed on vehicle systems. Based on a vehicle dynamics model of the vehicle with three degrees of freedom (3-DOFs), a union disturbance observer (UDO) composed of a nonlinear disturbance observer (NDO) and an extended state observer (ESO) was designed to obtain external disturbances and unmodeled items. Meanwhile, an improved adaptive unscented Kalman filter (iAUKF) with anti-disturbance and anti-noise properties is proposed, based on the UDO and the unscented Kalman filter (UKF) method, to evaluate the sideslip angle of vehicle systems. Finally, a vehicle yaw stability controller was designed based on UDO and the global fast terminal sliding mode control (GFTSMC) method. The results of co-simulation demonstrated that the proposed UDO was effectively able to observe external disturbances and unmodeled items. The proposed iAUKF, which considers external disturbances, not only achieves adaptive updating and adjustment of filtering parameters under different sensor noise intensities but can also resist external disturbances, improving the estimation accuracy and robustness of the UKF. In the anti-disturbance performance test, the maximum estimation error of the sideslip angle of the iAUKF under the three working conditions was less than 0.1°, 0.02°, and 0.5°, respectively. Based on the UDO and the GFTSMC, a vehicle yaw stability controller is described, which improves the accuracy of control and the robustness of the vehicle’s stability control system and greatly strengthens the driving safety of the vehicle.

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Vehicle state and parameter estimation based on adaptive robust unscented particle filter

Cited by 2 publications

References 15 publications

Vehicle dynamics prediction via adaptive robust unscented particle filter

Vehicle dynamics prediction via adaptive robust unscented particle filter

Research on Vehicle Stability Control Based on a Union Disturbance Observer and Improved Adaptive Unscented Kalman Filter

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