Abstract:

For the characteristic of significant load variation in urban logistics autonomous light trucks and to meet the needs of low computational load and high stability， a path-tracking control method based on Linear Parameter-Varying Model Predictive Control （LPV-MPC） is proposed in this paper. Firstly， a linear parameter-varying model is constructed， and nonlinear mapping rules between the model and scheduling variables - speed and load - are established， to improve driving stability and mitigating system sensitivity to parameter fluctuations. Then， for the rolling optimization stage， a trajectory reconstruction method is designed to reconcile disparities between the discrete trajectory points provided by the planning layer and the demand of the control module's prediction layer. A smooth trajectory sequence tailored to the temporal scale of the prediction layer is constructed to effectively decrease the deviation between predicted and actual states. In addition， a multi-point state deviation prediction method is used instead of the traditional single-point prediction， fully leveraging reference trajectory information for improved tracking accuracy. Finally， the effectiveness of the proposed control strategy is verified through combined simulation and empirical vehicle tests.

Key words: autonomous light truck, path tracking, LPV-MPC, trajectory reconstruction