A coupled force predictive control of vehicle stability using front/rear torque allocation with experimental verification

Hajiloo, Reza; Khajepour, Amir; Zengin, Halit; Kasaiezadeh, Alireza; Chen, Shih-Ken

doi:10.1080/00423114.2021.1912363

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…Regarding the coupling dynamics of the tire, this scenario can also be viewed as a robustness verification of the proposed controller. 54 The vehicle drives on a highway with merge avoidance action under the initial longitudinal v x = 125 km / h with a road coefficient of 0.85. The simulation results under this driving scenario are presented in Figure 13.…”

Section: Simulation Hil Test and Discussion Of Resultsmentioning

confidence: 99%

Model predictive control allocation based on adaptive sliding mode control strategy for enhancing the lateral stability of four-wheel-drive electric vehicles

Ao¹,

Wong²,

Huang

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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A novel hierarchical direct yaw moment controller is designed to enhance the lateral stability of the four-wheel-drive electric vehicle. The adaptive sliding mode control (ASMC) technique in the upper-layer controller is employed to compute an additional yaw moment. The lower-layer controller distributes this yaw moment into each independent wheel by utilizing model predictive control allocation (MPCA). The proposed MPCA aims to mitigate the performance deterioration induced by in-wheel motor dynamics and optimize the power consumption stemming from the additional yaw moment. Co-simulation and hardware-in-the-loop (HIL) test is conducted to verify the performance of the proposed controller. Validation results show that the proposed hierarchical ASMC-MPCA controller outperforms the sliding mode control MPCA (SMC-MPCA) and the integrated nonlinear model predictive control (NMPC) with the lowest root-mean-square errors [Formula: see text] of yaw rate, sideslip angle, lateral deviation, and lowest power consumption. Additionally, the chattering phenomenon in SMC-MPCA can be suppressed effectively by adaptively estimating the parameter uncertainties. The proposed ASMC-MPCA controller also consumes less computational resources than the NMPC and SMC-MPCA, which indicates that the ASMC-MPCA is more suitable for an automotive onboard controller. The comparison between hierarchical and integrated controller frameworks also shows that the hierarchical framework is more suitable for production vehicles under non-powerful vehicle control units.

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