Data-Driven Nonlinear Adaptive Optimal Control of Connected Vehicles

被引：0
|
作者：
Gao, Weinan [1 ]
Jiang, Zhong-Ping [2 ]
机构：
[1] Georgia Southern Univ, Allen E Paulson Coll Engn & Informat Technol, Dept Elect Engn, Statesboro, GA 30460 USA
[2] NYU, Tandon Sch Engn, Dept Elect & Comp Engn, Brooklyn, NY 11201 USA
来源：
NEURAL INFORMATION PROCESSING (ICONIP 2017), PT VI | 2017年 / 10639卷
基金：
美国国家科学基金会;
关键词：
Global adaptive dynamic programming; Cooperative adaptive cruise control; Nonlinear optimal control; Connected and autonomous vehicles;
D O I：
10.1007/978-3-319-70136-3_13
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 studies the cooperative adaptive cruise control (CACC) problem of connected vehicles with unknown nonlinear dynamics. Different from the existing literature on CACC, a data-driven optimal control policy is developed by global adaptive dynamic programming (GADP). Interestingly, the developed control policy achieves global stabilization of the nonlinear vehicular platoon system in the absence of the a priori knowledge of system dynamics. Numerical simulation results are presented to validate the effectiveness of the developed approach.
引用
收藏
页码：122 / 129
页数：8
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