Stabilizing Model Predictive Control for Autonomous Tracked Vehicles Trajectory Tracking Considering Time-Varying Characteristics

In order to realize high tracking accuracy, this paper proposes a model predictive control (MPC)-based trajectory tracking control strategy for electrified tracked vehicles. Considering the time-varying parameters such as the velocity, the slip ratio and the sideslip angle, the linear parameter varying (LPV) system is constructed to describe the motion of the tracked vehicle. Then the trajectory tracking strategy is proposed and can be classified into three modules: the slip parameter estimator, the terminal ingredients module and the rolling optimization module. The slip parameter estimator updates the slip parameters at each control interval. To ensure asymptotic stability of the tracking control system, the terminal ingredients are introduced and solved by the linear matrix inequalities (LMI). Then the rolling optimization module solves the quadratic constraint quadratic programming (QCQP)-based trajectory tracking problems in real time. The simulation results demonstrate that the proposed strategy can achieve accurate tracking performance while the computation burden is affordable.