Real-time analysis for nonlinear model predictive control of autonomous vehicles

This paper presents an online Nonlinear Model Predictive Control (NMPC) framework for trajectory tracking of autonomous vehicles. The operating environment is assumed to be unknown with various different types of obstacles. A bicycle model is used for the prediction of the future states in the NMPC framework, and a fully nonlinear CarSim vehicle model is used for the simulations. Real-time analysis is presented for a particular situation and the effect of warm initialization of optimization process on the computation time is elaborated. Simulation results show that the NMPC controller provides satisfactory online tracking performance while satisfying the real-time constraints, and warm initialization reduces the optimizer computational load significantly.