학술논문
A Unified Framework for Data-Driven Optimal Control of Connected Vehicles in Mixed Traffic
Document Type
Periodical
Source
IEEE Transactions on Intelligent Vehicles IEEE Trans. Intell. Veh. Intelligent Vehicles, IEEE Transactions on. 8(8):4131-4145 Aug, 2023
Subject
Language
ISSN
2379-8858
2379-8904
2379-8904
Abstract
This article presents a unified approach to the problem of learning-based optimal control of connected human-driven and autonomous vehicles in mixed-traffic environments including both the freeway and ring road settings. The stabilizability of a string of connected vehicles including multiple autonomous vehicles (AVs) and heterogeneous human-driven vehicles (HDVs) is studied by a model reduction technique and the Popov-Belevitch-Hautus (PBH) test. For this problem setup, a linear quadratic regulator (LQR) problem is formulated and a solution based on adaptive dynamic programming (ADP) techniques is proposed without a priori knowledge on model parameters. To start the learning process, an initial stabilizing control law is obtained using the small-gain theorem for the ring road case. It is shown that the obtained stabilizing control law can achieve general $\mathcal {L}_{p}$ string stability under appropriate conditions. Besides, to minimize the impact of external disturbance, a linear quadratic zero-sum game is introduced and solved by an iterative learning-based algorithm. Finally, the simulation results verify the theoretical analysis and the proposed methods achieve desirable performance for control of a mixed-vehicular network.