Abstract: In order to further improve unmanned hybrid vehicles trajectory tracking accuracy and energy consumption economy, this paper proposed a trajectory tracking energy-saving control fusion strategy. Firstly, the vehicle kinematics model was established, and the trajectory tracking control of the vehicle was carried out by using the model predictive control strategy. Then, with velocity as the interactive variable, a three-stage dynamic programming energy-saving control strategy was proposed. In this way, the optimal economic function was optimized online to reduce the total cost of energy consumption of the vehicles. Finally, the independent pure pursuit trajectory tracking algorithm and the power following energy-saving control were selected for comparison strategies. The results show that the proposed trajectory tracking energy-saving control fusion strategy improves the trajectory tracking effectvieness and reduces the total cost of vehicle energy consumption. The trajectory tracking errors are reduced 70.47%. The total cost of energy consumption decreases 4.52% and 25.10% in pure electric drive mode and hybrid drive mode, respectively.

Key words: unmanned vehicle, hybrid power, model predictive control, dynamic programming, trajectory tracking energy-saving control

摘要： 为进一步提高无人驾驶混合动力汽车轨迹跟踪精度和能耗经济性，提出了一种轨迹跟踪节能控制融合策略。首先，建立车辆运动学模型，采用模型预测控制策略对车辆进行轨迹跟踪控制；在此基础上，以速度为交互变量，提出了一种三阶段动态规划节能控制策略，在线优化最优经济性函数，以降低整车能耗总成本；最后，选择相互独立的纯跟踪轨迹跟踪算法与功率跟随节能控制策略进行比较。结果表明，所提出的轨迹跟踪节能控制融合策略提高了轨迹跟踪效果，降低了整车能耗总成本，轨迹跟踪精度提高了70.47%，纯电动和混合驱动模式下能耗总成本分别下降了4.52%和25.10%。

关键词: 无人驾驶汽车, 混合动力, 模型预测控制, 动态规划, 轨迹跟踪节能控制