Data-Driven Shared Steering Control of Semi-Autonomous Vehicles

被引:64
作者
Huang, Mengzhe [1 ]
Gao, Weinan [2 ]
Wang, Yebin [3 ]
Jiang, Zhong-Ping [1 ]
机构
[1] NYU, Control & Networks Lab, Dept Elect & Comp Engn, Tandon Sch Engn, Brooklyn, NY 11201 USA
[2] Georgia Southern Univ, Dept Elect & Comp Engn, Allen E Paulson Coll Engn & Comp, Statesboro, GA 30460 USA
[3] Mitsubishi Elect Res Labs, Cambridge, MA 02139 USA
基金
美国国家科学基金会;
关键词
Adaptive dynamic programming (ADP); human in the loop; shared driving; small gain; steering control; ITERATIVE LEARNING CONTROL; SMALL-GAIN THEOREM; COLLISION-AVOIDANCE; SYSTEMS; MODEL; FEEDBACK;
D O I
10.1109/THMS.2019.2900409
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a cooperative/shared framework of the driver and his/her semi-autonomous vehicle in order to achieve desired steering performance. In particular, a copilot controller and the driver together operate and control the vehicle. Exploiting the classical small-gain theory, our proposed shared steering controller is developed independent of the unmeasurable internal states of the human driver, and only relies on his/her steering torque. Furthermore, by adopting data-driven adaptive dynamic programming and an iterative learning scheme, the shared steering controller is studied from the measurable data of the driver and the vehicle. Meanwhile, the accurate knowledge of the driver and the vehicle dynamics is unnecessary, which settles the problem of their potential parametric variations/uncertainties in practice. The effectiveness of the proposed method is validated by rigorous analysis and demonstrated by numerical simulations.
引用
收藏
页码:350 / 361
页数:12
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