Research On Motion Control Of Intelligent Vehicle Based On Combined Lateral And Longitudinal Control

Intelligent vehicle is an important part of Intelligent Transportation Systems. The realization of autonomous driving can reduce the incidence of traffic accidents, enhance the capacity of the road, and reduce the fuel consumption when the vehicle starts and stops to make the environment better. The motion control of the intelligent vehicle is the basic problem in the research.First, for the low speed driving condition of the intelligent vehicle, a SA-MPC motion controller based on vehicle kinematic model for intelligent vehicle is proposed. Prediction model based on vehicle kinematic model is established, objective function and constraint condition are designed according to the control objective. Simulated annealing algorithm is used to solve the objective function, thus the control of the longitudinal speed and front wheel angle is realized. Simulation results show that the proposed algorithm performs effectively.Then, for the high speed driving condition of the intelligent vehicle on the low adhesion road, a lateral controller based on vehicle dynamic model for intelligent vehicle is designed. Linear 2-DOF vehicle dynamic model is taken as the basic vehicle dynamic model, on the basis of this, the prediction model is established. Then the weights of vehicle yaw rate and vehicle slip angle are added to the objective function for considering the vehicle stability. Constraint conditions including upper and lower bounds and inequality constraints are set by the realistic conditions. Moreover, the calculation methods for the reference value of vehicle yaw rate, vehicle slip angle and longitudinal position are discussed. Simulation results show that the proposed algorithm performs effectively.At last, in order to reflect the actual driving conditions of intelligent vehicle completely and verify the tracking performance of the controller above under the variable speed, a longitudinal controller is designed using Fuzzy PID method. Then the Fuzzy PID longitudinal controller and the MPC lateral controller are combined to build a simultaneous control framework, the reference speed during the moving of the intelligent vehicle is also designed. Simulation results show that the proposed algorithm performs effectively.