Human-machine Cooperative Control Framework for Intelligent Vehicle Considering Intervention Punishment

doi:10.3901/JME.2024.14.238

Abstract

Abstract: The uncertainties of human-machine interaction would cause conflicts between the driver and the intelligent assisted driving system, and thus deteriorating the vehicle driving performance. To enhance the drivability and lateral stability of the vehicle, an intelligent human-machine cooperative control framework, which considers the dynamic intervention penalty, is proposed. First, to well address the uncertainties in the human-machine shared driving system, the time-varying driver preview behavior and the tire nonlinear characteristics are considered in the vehicle system modelling; Second, to attenuate the conflicts between drivers and assistance steering actions due to the personalized driving behaviors, the human-machine intervention penalty factor is introduced into the driving authority allocation, and the fuzzy rule is established based on the dynamic driver torque and lateral deviation of actual preview point. Third, a linear parameter varying(LPV) controller based on the system poles placement is developed to improve the control system robustness. Finally, to verify the feasibility and effectiveness of the proposed control strategy, the Matlab/Carsim joint simulation and the hardware-in-loop(HIL) test based on NI-LabVIEW-RT system are conducted. The results show that the proposed human-machine cooperative control framework can effectively mitigate the human-machine conflict while guaranteeing the vehicle handling performance.

Key words: vehicle stability control, LPV robust control, human-machine cooperative control, vehicle system dynamics

CLC Number:

U461

FENG Jiwei, YIN Guodong, LIANG Jinhao, ZHUANG Weichao, PENG Pai, LU Yanbo, CAI Guoshun, XU Liwei. Human-machine Cooperative Control Framework for Intelligent Vehicle Considering Intervention Punishment[J]. Journal of Mechanical Engineering, 2024, 60(14): 238-251.

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