UKF Estimation Method of Centroid Slip Angle for Vehicle Stability Control

Wang, Pan; Fan, Xiaobin; Chen, Xinbo; Yi, Juean; He, Shuwen

doi:10.1007/s12555-022-0306-2

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…This can be typically achieved through extended Kalman filters (EKFs), which imply a linearisation at each time step, or unscented Kalman filters (UKFs), which directly use nonlinear models. Comparative studies of EKF and UKF performance for the same vehicle model, e.g., see [16,[20][21][22][23][24], highlight the superiority of UKFs, towards which there has been a progressive shift [17].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

Alshawi,

De Pinto,

Stano

et al. 2024

Sensors

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This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

Alshawi,

De Pinto,

Stano

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Research on ground mobile robot trajectory tracking control based on MPC and ANFIS

You,

Yang,

Zhuo

et al. 2024

Control Engineering Practice

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Robust joint estimation of sideslip angle and tire lateral force for distributed drive electric vehicle

Yu,

Chen,

Xiong

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part E:

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Accurate estimation of vehicle sideslip angle (SA) and tire lateral force (TLF) is essential for the effective functioning of vehicle active safety control systems. However, effective estimation of SA and TLF using low-cost on-board sensors is a major challenge. In this paper, a robust joint estimation method of SA and TLF for distributed drive electric vehicle (DDEV) is proposed. Adaptive sliding-mode observer (ASMO) is used to estimate the TLF and is used as the input to the subsequent filter. Maximum correntropy criterion (MCC) is introduced to improve the robustness of the unscented Kalman filter (UKF) under mixed Gaussian noise, and the maximum correntropy unscented Kalman filter (MCUKF) algorithm is composed to estimate SA. The effectiveness of the proposed ASMO and MCUKF joint estimation method is verified by Simulink/Carsim joint simulation experiments. The experimental results show that compared with the existing methods, the estimation method proposed in this paper effectively improves the estimation accuracy and robustness of SA and TLF under the mixed Gaussian environment, which is of great significance for the improvement of vehicle active safety.

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UKF Estimation Method of Centroid Slip Angle for Vehicle Stability Control

Cited by 3 publications

References 6 publications

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

Research on ground mobile robot trajectory tracking control based on MPC and ANFIS

Robust joint estimation of sideslip angle and tire lateral force for distributed drive electric vehicle

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