Abstract:

In order to ensure the accuracy and stability of the tracking control of intelligent vehicles under low attachment and variable speed conditions, a trajectory tracking control algorithm based on adaptive Model Predictive Control（MPC） is proposed. Firstly, aiming at the problems of trajectory tracking in low attachment environment, a soft constraint of the slip angle is added to the vehicle dynamics model. It designs the MPC controller with or without side slip angle constraint. The simulation results show that the performance of the MPC controller is better after the slip angle constraint is added, and the vehicle driving stability is effectively improved. Then, on this basis, an adaptive trajectory tracking control strategy is proposed, which can generate the predicted time domain [(Hp)] in real time according to the change of vehicle speed, and design an adaptive MPC controller and MPC controller of four sets of fixed [Hp] respectively. The simulation results show that the trajectory tracking control algorithm based on the adaptive model predictive control has certain effectiveness and advancement in solving the intelligent vehicle trajectory tracking control under low adhesion and variable speed conditions.

Key words: intelligent vehicle, adaptive, model predictive control, trajectory tracking

摘要：

为了保证智能车辆在低附着且变速条件下跟踪控制的精确性和稳定性，提出一种基于自适应模型预测控制（MPC）的轨迹跟踪控制算法。针对低附着条件下轨迹跟踪存在行驶稳定性较差的问题，对车辆动力学模型添加侧偏角软约束，分别设计有无添加侧偏角约束的MPC控制器。仿真结果表明，添加侧偏角约束后MPC控制器性能更优，车辆行驶稳定性得到有效提高。在此基础上，又提出了一种自适应的轨迹跟踪控制策略，能够根据车辆速度的变化，实时产生预测时域[(Hp)]，分别设计自适应的MPC控制器与4组定值[Hp]的MPC控制器。仿真结果表明，基于自适应模型预测控制的轨迹跟踪控制算法在提高低附着且变速条件下智能车辆轨迹跟踪控制的精度和稳定性方面具有一定的有效性和先进性。

关键词: 智能车辆, 自适应, 模型预测控制, 轨迹跟踪