Implementation of MPC-Based Trajectory Tracking Considering Different Fidelity Vehicle Models

Abstract

In order to investigate how model fidelity in the formulation of model predictive control(MPC) algorithm affects the path tracking performance, a bicycle model and an 8 degrees of freedom(DOF) vehicle model, as well as a 14-DOF vehicle model were employed to implement the MPC-based path tracking controller considering the constraints of input limit and output admissibility by using a lower fidelity vehicle model to control a higher fidelity vehicle model. In the MPC controller, the nonlinear vehicle model was linearized and discretized for state prediction and vehicle heading angle, lateral position and longitudinal position were chosen as objectives in the cost function. The wheel step steering and sine wave steering responses between the developed vehicle models and the Carsim model were compared for validation before implementing the model predictive path tracking control. The simulation results of trajectory tracking considering an 8-shaped curved reference path were presented and compared when the prediction model and the plant were changed. The results show that the trajectory tracking errors are small and the tracking performances of the proposed controller considering different complexity vehicle models are good in the curved road environment. Additionally, the MPC-based controller formulated with a high-fidelity model performs better than that with a low-fidelity model in the trajectory tracking.

Keywords

model predictive control / vehicle dynamics / path tracking / autonomous vehicle