Horizon-wise Model Predictive Control with Application to Autonomous Driving Vehicle

Abstract

In this article, we present an innovative approach, i.e., horizonwise model-predictive control (H-MPC), to solve the model-predictive control (MPC) problem of a linear time-varying (LTV) system. In H-MPC, we regard the time-varying parameters as time invariant within the prediction horizon. To solve the MPC problem of the time-varying system, the decision variable is decomposed into two terms: one for linear time-invariant optimization and the other for compensating LTV uncertainties with an introduction to a uniform compensation condition. The proposed H-MPC solves the time-varying problem by removing the uncertainty due to the future parameter variations within the horizon and by updating the time-invariant MPC at each sampling time. To validate the usefulness of the proposed H-MPC, it is applied to lane tracking control for an autonomous driving vehicle. From a comparative study of the H-MPC and conventional MPCs in lane tracking control, it is confirmed that the proposed H-MPC has a competitive performance compared to LTV-MPC despite its much simpler structure.