运用路径动态预览模型的低速智能汽车侧向跟踪控制研究
Research on Lateral Tracking Control of Low Speed Intelligent Vehicle Using Dynamic Path Preview Model
王丽娟 1关龙新 2张明华 2时乐泉 3王爱春 3吴晓建2
作者信息
- 1. 南昌大学 机电工程学院,南昌 330031
- 2. 南昌大学 机电工程学院,南昌 330031;江铃汽车股份有限公司,南昌 330001
- 3. 江铃汽车股份有限公司,南昌 330001
- 折叠
摘要
采用速度自适应的动态预瞄距离是提高智能车侧向跟踪控制效果的有效办法.针对目前基本采用基于当前车速的预瞄距离自适应策略,提出一种结合规划路径和规划速度信息的动态预瞄距离跟踪控制算法以提高智能车在低速侧向跟踪控制的精确性.本文以几何学纯跟踪算法为基础,推导了前行和倒车时的侧向跟踪控制律;依据实时规划的路径和速度信息,设计了预瞄距离动态调整方法,最终获取具有速度自适应性的前视距离-车辆前轮转角关系.最后,在 CarSim-MATLAB/Simulink联合仿真环境下验证了算法的有效性和准确性,并通过实车测试,验证了所提出的方法较基于动力学模型的LQR算法具有更低计算消耗和更高跟踪精度.
Abstract
Speed adaptive dynamic preview distance is an effective way to improve the lateral tracking control effect of intelligent vehicle,and as for the problem of using current vehicle speed for the preview distance,a dynamic preview distance tracking control algorithm combining planned path and planned speed information is proposed to improve the accuracy of path tracking at low speed.Based on the pure pursue algorithm,the lateral tracking control laws of moving forward and reversing are derived.According to the real-time planned path and speed information,a dynamic adjustment method of preview distance was designed,and the relationship between the preview distance and the steering angle with speed adaptability was thereby obtained.Finally,the effectiveness and accuracy of the proposed algorithm were verified in CarSim-MATLAB/Simulink co-simulation environment,and a real vehicle test was performed,indicating that the proposed algorithm has lower computational consumption and higher tracking accuracy than the LQR algorithm based on dynamics model.
关键词
智能汽车/侧向跟踪/动态预瞄距离/仿真与实车验证Key words
intelligent vehicle/lateral tracking/dynamic preview distance/simulation verification and real vehicle validation引用本文复制引用
基金项目
国家自然科学基金项目(52002163)
国家自然科学基金项目(52062036)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据