Intelligent Vehicle Path Planning and Tracking Control based on MPC
The dynamic planning effect of obstacle avoidance paths in obstacle environments was poor and the tracking control effect was still not ideal when facing complex working conditions and high curvature road conditions.Taking intelligent vehicles as the research object,a path planning and tracking system was proposed by combining model predictive control(MPC)algorithm with artificial potential field(AMF)algorithm.The improved potential field model function was intrduced into the objective function and constraints of MPC.The dynamic obstacle avoidance path planner based on MPC and improved APF was designed.Fuzzy control was used to optimize the weight coefficients in the MPC path tracking controller.The simulation results show that the maximum lateral deviation of the fuzzy MPC path tracking controller is reduced by 19.14％compared with the MPC controller on dry road.The maximum lateral deviation of the fuzzy MPC controller is reduced by 0.55 m on wet road.The joint simulation model of obstacle avoidance path planning and tracking control is built based on MATLAB/Simulink and Carsim software.Dynamic obstacle path planning and tracking control simulation experiments are conducted under different speeds of dynamic obstacles.The experimental results show that the maximum lateral deviation in the process of tracking the planned path is about 0.170 m,which indicates that the planned obstacle avoidance path can avoid obstacles safely and effectively.
intelligent drivingmodel predictive controlartificial potential field methodfuzzy control
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