Autonomous Vehicle Tracking Based on Non-Linear Model Predictive Control Approach

Abstract

Autonomous driving vehicles are developing rapidly; however, the control systems for autonomous driving vehicles tracking smoothly in high speed are still challenging. This chapter develops non-linear model predictive control (NMPC) schemes for controlling autonomous driving vehicles tracking on feasible trajectories. The optimal control action for vehicle speed and steering velocity is generated online using NMPC optimizer subject to vehicle dynamic and physical constraints as well as the surrounding obstacles and the environmental side-slipping conditions. NMPC subject to softened state constraints provides a better possibility for the optimizer to generate a feasible solution as real-time subject to online dynamic constraints and to maintain the vehicle stability. Different parameters of NMPC are simulated and analysed to see the relationships between the NMPC horizon prediction length and the weighting values. Results show that the NMPC can control the vehicle tracking exactly on different trajectories with minimum tracking errors and with high comfortability.