Cascaded Disturbance Compensation for MPC-based Autonomous Vehicle Guidance

Abstract

This paper investigates the task of lateral disturbance compensation based on model predictive control (MPC) for autonomous vehicles. By considering external disturbances and parameter perturbations in the model term of the MPC, the steady-state offset can be compensated. However, in the presence of more dynamic disturbances, like side wind, the lateral path tracking performance deteriorates. To overcome this limitation, a cascaded approach is presented, which is a combination of an MPC-based and an underlying direct compensation. The performance of this approach is validated in simulations as well as in practice with real vehicle tests.