Trajectory Tracking of Unmanned Ground Vehicle Based on Iterative Learning Model Predictive Control

Abstract

In this paper, an iterative learning model predictive control (ILMPC) strategy is introduced for the trajectory tracking of Unmanned Ground Vehicle (UGV). First, a linear time-varying (LTV) system of UGV is derived from the simplified dynamic vehicle model of UGV by Taylors formula. Second, the constrained ILMPC controller is introduced to solve the trajectory tracking problem, which is described as a QP problem. Finally, a simulation about trajectory tracking of batch process is presented to show the effectiveness of the proposed controller.