Traction control based on wheel slip tracking of a quarter-vehicle model with high-gain observers

Le, Duc Thinh; Nguyen, Duy–Cuong; Le, Nam Duong; Nguyễn, Tùng Lâm

doi:10.1007/s40435-021-00881-6

Cited by 10 publications

(1 citation statement)

References 16 publications

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“…Wang et al proposed a method of tire road friction coefficient estimation with review and research perspectives [16]. Duc et al introduced an anti-slip controller for a quarter vehicle traction control system [17]. Karnika et al proposed a method aiming at establishing an alternative approach to dynamic modeling and robust control with online estimation of slip parameters [18].…”

Section: Introductionmentioning

confidence: 99%

Vehicle state and parameter estimation based on adaptive anti-outlier unscented Kalman filter and GA-BPNN method

Liu,

Cui,

Peng

2023

J. vibroeng.

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A multi-machine-learning improved adaptive Kalman filtering method is proposed to address the problem of handling abnormal data encountered in the vehicle state estimation. Firstly, the unscented Kalman filter (UKF) algorithm is improved by introducing a BP neural network improved by the genetic algorithm (GA-BPNN) to regulate and correct the global error of the UKF method. Then, the anti-outlier technique is applied to fully eliminate isolated and speckled outliers in the measurement, achieving further improvement on GA-BPNN-UKF and significantly improving the robustness of the filtering process. Finally, a simulation is applied to verify the effectiveness of the proposed new algorithm, and then its results are analyzed to obtain a firm substantiation of its effectiveness for further practical applications. The simulation results indicate that the estimation performance of the GA-BPNN algorithm is significantly better than that of Extended Kalman filter (EKF) method.

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Section: Introductionmentioning

confidence: 99%