Integrated Control for Path Tracking and Stability Based on the Model Predictive Control for Four-Wheel Independently Driven Electric Vehicles

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Abstract
Four-wheel independently driven electric vehicles are prone to rollover when driving at high speeds on high-adhesion roads and to sideslip on low-adhesion roads, increasing the risks associated with such vehicles. To solve this problem, this study proposes a path tracking and stability-integrated controller based on a model predictive control algorithm. First, a vehicle planar dynamics model and a roll dynamics model are established, and the lateral velocity, yaw rate, roll angle, and roll angle velocity of the vehicle are estimated based on an unscented Kalman filter. The lateral stiffness of the tires is estimated online according to the real-time feedback state of the vehicle. Then, the path tracking controller, roll stability controller, and lateral stability controller are designed. An integrated control strategy is designed for the path tracking and stability, and the conditions and coordination strategies for the vehicle roll and lateral stability state in the path tracking are studied. The simulation results show that the proposed algorithm can effectively limit the lateral load transfer rate on high-adhesion roads and the sideslip angle on low-adhesion roads at high speeds. Hence, the driving stability of the vehicle under different road adhesion coefficients can be ensured and the path tracking performance can be improved.
Funding Information
  • Natural Science Foundation of Guangxi Province (2020GXNSFAA297031)
  • National Natural Science Foundation of China (52262052)

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