A direct yaw moment control frame through model predictive control considering vehicle trajectory tracking performance and handling stability for autonomous driving

This paper considers the problem of optimal coordination of trajectory tracking performance and handling stability for autonomous equipped with distributed drive electric vehicle. Therefore, a hierarchical frame for multi -mode chassis dynamics torque vector allocation strategy is proposed, which aimed to solve the contradictory issues between vehicles' trajectory tracking accuracy and handling stability under extreme working conditions. Firstly, in a hierarchical architecture, the upper -level trajectory tracking controller is designed by using model predictive control theory, which is used to solve the front wheel angle and the additional yaw moment of the vehicle. Secondly, the lower -level multimode torque distribution controller severs the sum of tire force utilization in every wheel as the objective function, and designs three distribution modes of chassis dynamic torque vectors based on the response of the longitudinal force and yaw moment obtained from the upper -level controller. Thirdly, the switching mechanism between the three chassis torque vector distribution modes is set according to the road adhesion condition and the requirements of the upperlevel controller. Then, an analysis is conducted on the computational time complexity and robustness of the algorithm, confirming the potential for real -world application of the algorithm. Finally, Simulink/CarSim co -simulation test and hardware -in -the -loop test platform are carried out. And a vehicle trajectory tracking controller with single -mode torque vectors distribution by MPC is used as the baseline algorithm. The test results show that the proposed method show better trajectory tracking performance and handling stability than the baseline algorithm under the conditions of low adhesion surfaces and split -friction surfaces. Therefore, this study provides a solution for the safe driving of autonomous vehicles under extreme working conditions.