Path Tracking Method of Intelligent Vehicle Based on Multi-Constrained Stochastic Model Predictive Control

Abstract:

A path tracking method of intelligent vehicle based on stochastic model predictive control is proposed. The predicted trajectories of surrounding dynamic vehicles are characterized by positional uncertainty using a prime motion model and Gaussian distribution in the road coordinate system， and described using chance constraints in stochastic model predictive control （SMPC） as a way to establish constraints on the spatial location of the vehicles. The initial control sequence based on the kinematic model is obtained by means of a variable step size solution. Based on this initial solution， a stability constraint based on the relationship between the angular velocity of the transverse pendulum and the lateral eccentricity of the center of mass is introduced by considering the vehicle dynamics information to solve the optimal control volume. The effectiveness and stability of the proposed method are verified by simulation tests under various operating conditions.