Constrained optimization and distributed computation based car following control of a connected and autonomous vehicle platoon

作     者：Gong, Siyuan Shen, Jinglai Du, Lili 

作者机构：IIT Dept Civil Architectural & Environm Engn Chicago IL 60616 USA Univ Maryland Baltimore Cty Dept Math & Stat Baltimore MD 21250 USA 

出 版 物：《TRANSPORTATION RESEARCH PART B-METHODOLOGICAL》 (运输研究B部分：方法)

年 卷 期：2016年第94卷

页      面：314-334页

核心收录：

学科分类：0201[经济学-理论经济学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0814[工学-土木工程] 0823[工学-交通运输工程] 

基　　金：National Science Foundation [CMMI-1436786, CMMI-1554559] Directorate For Engineering Div Of Civil, Mechanical, & Manufact Inn [1554559, 1436786] Funding Source: National Science Foundation 

主　　题：Connected and autonomous vehicles Car-following control Optimization Distributed algorithm 

摘      要：Motivated by the advancement in connected and autonomous vehicle technologies, this paper develops a novel car-following control scheme for a platoon of connected and autonomous vehicles on a straight highway. The platoon is modeled as an interconnected multi-agent dynamical system subject to physical and safety constraints, and it uses the global information structure such that each vehicle shares information with all the other vehicles. A constrained optimization based control scheme is proposed to ensure an entire platoon s transient traffic smoothness and asymptotic dynamic performance. By exploiting the solution properties of the underlying optimization problem and using primal-dual formulation, this paper develops dual based distributed algorithms to compute optimal solutions with proven convergence. Furthermore, the asymptotic stability of the unconstrained linear closed-loop system is established. These stability analysis results provide a principle to select penalty weights in the underlying optimization problem to achieve the desired closed-loop performance for both the transient and the asymptotic dynamics. Extensive numerical simulations are conducted to validate the efficiency of the proposed algorithms. (C) 2016 Elsevier Ltd. All rights reserved.