Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities

被引:326
作者
Li, Shengbo Eben [1 ]
Zheng, Yang [2 ]
Li, Keqiang [1 ]
Wu, Yujia [3 ]
Hedrick, J. Karl [3 ]
Gao, Feng [4 ]
Zhang, Hongwei [5 ]
机构
[1] Tsinghua Univ, Dept Automot Engn, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China
[2] Univ Oxford, Dept Engn Sci, Parks Rd, Oxford OX1 3PJ, England
[3] Univ Calif Berkeley, Dept Mech Engn, Berkeley, CA 94720 USA
[4] Chongqing Univ, Sch Elect Engn, Chongqing 400044, Peoples R China
[5] Wayne State Univ, Dept Comp Sci, Detroit, MI 48202 USA
基金
美国国家科学基金会;
关键词
ADAPTIVE CRUISE CONTROL; STRING STABILITY; PLATOON CONTROL; DISTURBANCE PROPAGATION; EXPERIMENTAL VALIDATION; INFORMATION-FLOW; TRAFFIC-FLOW; DESIGN; SAFETY; BRAKING;
D O I
10.1109/MITS.2017.2709781
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g., enhancing highway safety, improving traffic utility, and reducing fuel consumption. Requiring only local information, distributed control schemes are scalable approaches to the coordination of multiple CAVs without using centralized communication and computation. From the perspective of multi-agent consensus control, this paper introduces a decomposition framework to model, analyze, and design the platoon system. In this framework, a platoon is naturally decomposed into four interrelated components, i.e., 1) node dynamics, 2) information flow network, 3) distributed controller, and 4) geometry formation. The classic model of each component is summarized according to the results of the literature survey; four main performance metrics, i.e., internal stability, stability margin, string stability, and coherence behavior, are discussed in the same fashion. Also, the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributed sliding mode control, and distributed model predictive control.
引用
收藏
页码:46 / 58
页数:13
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