引用本文:李文宇,崔冀宁,段峰.基于反步法的四轮车体跟踪控制半实物仿真研究[J].控制与决策,2021,36(1):90-96
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基于反步法的四轮车体跟踪控制半实物仿真研究
李文宇1, 崔冀宁2, 段峰1
(1. 南开大学人工智能学院,天津300350;2. 太原卫星发射中心,山西忻州036300)
摘要:
人机共驾是非自动驾驶迈向全自动驾驶的中间过渡技术,其半实物仿真能显著减少实车的实验消耗.针对不具备四轮车体模型的仿真平台以及传统坐标系转换方法的局限,基于两轮差速移动车体模型和四轮车体模型的位姿状态误差,利用一种非线性反步控制方法,实现对四轮车体模型运动轨迹的有效实时跟踪.通过方向盘和踏板在虚拟现实环境下进行人机共驾模拟,为开发更逼真的人机共驾及模拟辅助驾驶系统提供了参考.以车体前进方向速度和导向轮角度作为系统输入,通过考察两轮差速移动车体和四轮车体的位姿状态误差,分别在数值仿真和半实物仿真实验条件下,对比并验证了所提出方法的有效性,行驶方向上10km的平均累积误差为4.56m.
关键词:  人机共驾  半实物仿真  非线性控制  轨迹跟踪
DOI:10.13195/j.kzyjc.2019.0471
分类号:TP273
基金项目:国家重点研发计划政府间国际科技创新合作重点专项项目(2017YFE0129700);国家自然科学基金面上项目(61673224);天津市自然科学基金杰出青年基金项目(18JCJQJC46100).
Tracking control for four-wheel vehicle semi-physical simulation based on back-stepping method
LI Wen-yu1,CUI Ji-ning\2,DUAN Feng1
(1. College of Artificial Intelligence,Nankai University,Tianjin300350,China;2. Taiyuan Satellite Launch Center,Xinzhou036300,China)
Abstract:
Human-vehicle collaborative driving(HVCD) is a transitional technology of non-autonomous/autonomous driving. The semi-physical simulation can significantly reduce the experiment consumption of real vehicles. However, most simulation platforms are lacking four-wheel vehicle models and certain limitations in conventional coordinate system transformation methods. To fill the gap, this paper uses a nonlinear back-stepping control method based on the pose errors between the two-wheel differential mobile vehicle model and the four-wheel vehicle model, which achieves efficient and real-time tracking for the four-wheel vehicle motion trajectory. Physical steering wheel and pedals are applied for HVCD simulation under virtual reality environment, which provides reference for the development of the more realistic system. Vehicle forwarding velocity and guide wheel angle are utilized as the system input. Experiments are processed to compare and verify the method validity under both numerical and semi-physical simulation environments. Through the investigation of the pose errors between the two-wheel differential mobile vehicle and the four-wheel vehicle, the average accumulate error in heading direction is 4.56m in 10km.
Key words:  human-vehicle collaborative driving  semi-physical simulation  nonlinear control  trajectory tracking

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