摘要
面向载人月球车自动驾驶任务需求,为解决月面低重力、低附着环境下的轨迹跟踪与稳定控制问题,提出一种基于强化学习的线性二次调节控制(LQRC)参数优化策略。首先,基于车辆动力学模型设计线性二次调节(LQR)控制器,对前后轮转向角和附加横摆力矩进行控制,融合预瞄点误差模型以适应月球车转向机构动态响应约束;其次,设计基于柔性动作-评价(SAC)算法的强化学习框架,构造以最优跟踪精度和质心侧偏角为目标的奖励函数,通过训练得到了实时优化LQR权重系数和预瞄点距离的智能体;最后,在Simulink环境中搭建了整车仿真模型和不同曲率的双移线测试工况。结果表明:强化学习方法相比固定参数控制,其横向位置误差分别减小28.1%和59.2%,质心侧偏角分别减小6.2%和29.8%,表示强化学习策略能够显著提升载人月球车跟踪精度和整车稳定性,为在月面复杂环境中实现自动驾驶提供了一种解决方案。
To meet the requirements of autonomous driving tasks of lunar rover vehicles and address the issues of path tracking and stability control in the lunar surface environment with low gravity and low adhesion,a strategy for optimizing linear quadratic regulator control(LQRC)parameters based on reinforcement learning is proposed.First,an linear quadratic regulator(LQR)controller is designed based on the vehicle dynamics model to control the front and rear wheel steering angles and additional yaw moment,and the preview point error model is integrated to adapt to the dynamic response constraints of the steering mechanism of lunar rover vehicles.Second,a reinforcement learning framework based on the soft actor-critic(SAC)algorithm is developed,and a reward function for achieving the optimal tracking accuracy and the sideslip angle is constructed.Through training,an intelligent agent capable of optimizing the LQR weight coefficients and preview point distance is obtained.Finally,a full-vehicle simulation model and double lane change test conditions with different curvatures are built in the Simulink environment.The results show that,compared with fixed parameter control,the reinforcement learning method reduces the lateral position errors by 28.1%and 59.2%and the sideslip angles by 6.2%and 29.8%,respectively.This indicates that the reinforcement learning strategy proposed in this paper can significantly improve the path tracking accuracy and stability control of lunar rover vehicles,providing a solution for realizing autonomous driving in the complex lunar surface environment.
作者
谷程鹏
张文奇
寿星
王卫军
施飞舟
GU Chengpeng;ZHANG Wenqi;SHOU Xing;WANG Weijun;SHI Feizhou(National Key Laboratory of Aerospace Mechanism,Shanghai 201108,China;Shanghai Institute of Aerospace System Engineering,Shanghai 201109,China)
出处
《上海航天(中英文)》
2026年第1期159-168,共10页
Aerospace Shanghai(Chinese&English)
关键词
载人月球车
轨迹跟踪
稳定控制
线性二次调节(LQR)
强化学习
lunar rover vehicle
path tracking
stability control
linear quadratic regulator(LQR)
reinforcement learning
