Trajectory Tracking for Unmanned Vehicle Based on Model Predictive Control Using Linear Matrix Inequalities

This paper researches the problem of trajectory tracking of the unmanned vehicle on the basis of the Model Predictive Control (MPC) approach. The dynamic behavior of the unmanned vehicle system is predicted by establishing a linear tracking error model. The model predictive controller is addressed through an infinite horizon optimization problem which specifically considers the constraints on the rear axle center speed and the deflection angle of front wheel of the vehicle. The overall MPC optimization problem is converted into a convex optimization problem, which is dealt with by the Linear Matrix Inequality (LMI) technique. The effectiveness of the provided strategy is verified by the co-simulation platform of MATLAB and Carsim.