Route Planning and Tracking Control of an Intelligent Automatic Unmanned Transportation System Based on Dynamic Nonlinear Model Predictive Control

Tracking control is one of the important working conditions of unmanned driving and can help vehicles keep distances and run in an orderly manner to improve traffic utilization, ensure clear roads and avoid rear-end collisions. This paper constructs the motion trend of obstacles within the predictive step length of model predictive control (MPC), designs the danger level indicator between vehicles and obstacles, and formulates dynamic planning for the original reference path based on MPC. This paper proposes an integrated collision avoidance control strategy based on dynamic nonlinear MPC (DNMPC), and predicts the locations of moving obstacles within the predictive step length of DNMPC. The proposed method can perform initial planning for the collision-avoidance path according to the road environment information and based on the activation function. It builds the function for the tendency of obstacles within the predictive step length of MPC, introduces it into the objective function for optimization, designs dynamic path planning control based on the theories of MPC, and performs local optimization to the initial reference path under the obstacles in motion with a mass model. In addition, it defines varying discrete step lengths within the predictive step length and achieves the long-distance prediction and high-precision control of collision avoidance controllers. The experimental results show that the dynamic, nonlinear, and integrated collision avoidance control proposed in this paper can ensure excellent collision avoidance and steady vehicle driving and has very good practical value.

Automated summary

This paper proposes an integrated collision avoidance control strategy based on MPC and DNMPC to achieve dynamic path planning and high-precision control by predicting the movement of obstacles within the predictive step length.