基于Frenet坐标系和控制延时补偿的智能车辆路径跟踪

The path tracking problem for intelligent vehicle with delayed control inputs is studied. The cramping angle is expressed as a series structure model with pure lag and first-order inertial delay, and a steering control delay model is established using Matlab/Simulink. The collected steering control data of an actual vehicle is analyzed for parameter identification of the proposed delay model.The equivalent delay performance in simulation environment based on V-REP and ROS is implemented. The model predictive control (MPC)-based path tracking controllers without or with considering delay control are designed based on Frenet coordinates, and the kinematic and dynamics models, which can also be used for marching vehicle formation. A curvature-variant reference paths collected at 5, 10 and 20 m/s are set in V-REP simulation environment. Three curvature-variant reference paths are presented. For the MPC path tracking controller without delay modeling, the average tracking error is less than 0.22 m for a vehicle platform without control delay. The MPC controllers with and without delay modeling are tested to compare their tracking performances for the vehicle system with long control delay. Simulated results indicate that the average and maximum tracking errors of MPC controller with delay modeling are 83.7% and 74.4% less than those of MPC controller without delay modeling when they are used on a vehicle with delayed control inputs. The kinematics-based MPC controller performs better at low speed, whereas the dynamics-based MPC controller performs better at high speed. Only dynamics-based MPC controller with delay modeling completed the whole test safely at 20 m/s on the vehicle with delayed control.