Research On Trajectory Tracking Control Of Distributed Drive Intelligent Vehicles

Trajectory tracking control is one of the key technologies in the field of autonomous driving.Due to the feature of independently controllable wheels,distributed drive vehicles are more conducive to intelligent vehicle control.This paper focuses on distributed drive vehicles,and investigates the trajectory tracking control of these vehicles to address the issues of tracking accuracy and driving safety at different speeds.The main research contents of this paper are as follows:Firstly,to reduce the complexity of the vehicle model,an overall vehicle dynamic model that reflects the lateral,longitudinal,and yaw dynamics of the vehicle is constructed.Based on the needs of the designed simulation vehicle,a hub motor model is established in Simulink and relevant control strategies are set.By building a joint simulation platform of Carsim and Simulink,simulation verification conditions are provided for the designed control strategies.Secondly,based on the simplified overall vehicle dynamic model,a model predictive control(MPC)based distributed drive vehicle trajectory tracking controller is established.In order to address the issues of trajectory tracking and driving stability under different speeds,which cannot be solved by a fixed prediction time horizon,the MPC trajectory tracking control for the model under different speeds is analyzed.The optimal correspondence between vehicle speed and prediction time horizon is found,and a prediction time horizon adaptive control is achieved through data fitting,which adjusts the output value of the prediction time horizon according to the speed changes.The designed variable prediction time horizon trajectory tracking controller is validated using the joint simulation platform of Carsim and Simulink,and the results show that the designed controller can ensure the trajectory tracking control accuracy and stability of the vehicle under different speeds.Finally,based on the variable predictive horizon trajectory tracking controller,two trajectory tracking control optimization schemes were designed for vehicles at different speeds.When the vehicle is traveling at low to medium speeds,the expected yaw rate is obtained based on the trajectory tracking error model.A differential degree lookup table model is established based on the relationship between vehicle speed,yaw rate,and differential degree,and the expected yaw rate is input into the lookup table model to obtain the differential degree of the inner and outer wheels of the vehicle.Based on the Ackermann model and the differential degree,the wheel speeds of the four wheels can be adjusted to reduce the lateral and yaw errors of the vehicle.When the vehicle is traveling at high speeds,a differential steering dynamic model is established to calculate the ideal vehicle driving state variables.Based on the LQR control strategy,an upper-level differential steering controller is designed.A four-wheel torque allocation controller is established based on the demand for longitudinal driving torque and lateral moment,combined with the problem of wheel torque distribution.The two trajectory tracking optimization control schemes for different speeds were validated using the Carsim and Simulink joint simulation platform.The results show that both schemes can reduce lateral and yaw errors of the vehicle at different speeds,and ensure the driving safety of the vehicle when the original steering system fails.