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

• Published in: Transportation research. Part B: methodological Vol. 94; pp. 314 - 334
• Main Authors: Gong, Siyuan, Shen, Jinglai, Du, Lili
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.12.2016; Elsevier Science Ltd
• Subjects: Algorithms; Asymptotic properties; Automobiles; Automotive engineering; Autonomous cars; Autonomous vehicles; Car following; Car-following control; Computer simulation; Connected and autonomous vehicles; Constraints; Distributed algorithm; Dynamical systems; Dynamics; Environmental impact; Mathematical models; Multiagent systems; Optimization; Smoothness; Stability analysis; Sustainable development; Traffic congestion; Connected and autonomous vehicles; Car-following control; Optimization; Distributed algorithm
• ISSN: 0191-2615; 1879-2367
• DOI: 10.1016/j.trb.2016.09.016

•Systematic car-following control algorithm for a connected and autonomous vehicle platoon.•Distributed algorithm solving a convex optimization with coupled constraints.•Stability analysis to demonstrate the desired transient and asymptotic dynamics. 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.