Research on Trajectory Tracking of Unmanned Vehicle Based on Soft Constraint Model Predictive Control

To improve the driving stability of unmanned vehicles and the accuracy of trajectory tracking on rainy and snowy days, this paper carries out an in-depth study on unmanned vehicles under the working condition of low adhesion coefficient of the road surface on rainy and snowy days. Firstly, this paper adopts the vehicle dynamics model and uses the model predictive control algorithm to design the prediction model of the vehicle. Secondly, the front wheel side deflection constraint is introduced into the model predictive control algorithm to ensure the stability of the vehicle under the low traction coefficient working condition and high precision trajectory tracking. Finally, the feasibility and effectiveness of the improved algorithm are verified by joint simulation on the Carsim-Simulink platform. The results show that compared with the traditional MPC vehicle model, the vehicle with the soft front wheel offset constraint has better tracking accuracy and driving stability.