Lateral And Longitudinal Cooperative Control Of Intelligent Vehicle Using Model Predictive Control

With the rapid development of big data,artificial intelligence and 5G technology,automobiles are also moving towards the "new four modernizations" of electrification,intelligence,connectivity and sharing,which is important for solving traffic jams,energy saving and environmental protection problems.As one of the key technologies of intelligent vehicle,motion control can ensure the accuracy and stability of trajectory following and speed following of the vehicle under complex working conditions.So,this article uses Model Predictive Control(MPC)theory to study motion control technology.First of all,a lateral tracking-error model is established based on linear two-degreeof-freedom vehicle dynamics model.At the same time,in view of the model mismatch problem caused by changes in internal parameters when the vehicle is driving under high speed conditions,the error between the vehicle state value and the predicted value is used as the correction term of the model,which improves the robustness of vehicle trajectory tracking.The simulation results show that,compared with ordinary MPC controllers,the MPC lateral controller designed in this paper has better tracking accuracy,and is also more robust to model mismatch caused by changes in vehicle internal parameters.Secondly,in order to further improve the trajectory tracking effect of the vehicle,a longitudinal layered controller is designed.According to the reference trajectory,the exponential model is used for speed planning.The upper controller uses Model Predictive Control(MPC)to solve the expected acceleration,and the lower controller considers the current speed deviation and compensates it based on PID control theory.Then,the lower controller converts the expected acceleration into the driving or braking action of the vehicle through the established inverse dynamic model.The simulation results show that the longitudinal controller can achieve good speed following under different working conditions.Thirdly,the horizontal and the vertical control are coupled based on the longitudinal velocity,and the expected longitudinal acceleration is input to the horizontal controller as a known input in each predicted time domain to form a vertical and horizontal integrated controller.The simulation results show that under different vehicle speeds,the vertical and horizontal integrated controller designed in this paper has better trajectory tracking effect compared with separate lateral control.At the same time,it has good robustness for different vehicle speeds and road conditions.Finally,through the intelligent driving hardware-in-the-loop test platform of the research group,the results show that the vertical and horizontal integrated controller proposed in this paper can track the desired trajectory at different vehicle speeds and meet the real-time requirements.