Learning Optimal Robust Control of Connected Vehicles in Mixed Traffic Flow

TL;DR

This paper develops a robust control method for connected vehicles in mixed traffic, using policy iteration within an H_infty framework to improve traffic flow stability amid human-driven vehicle behaviors.

Contribution

It introduces a novel policy iteration algorithm for optimal robust control of mixed traffic systems, addressing nonlinear human driver behaviors.

Findings

Controllers effectively dampen traffic disturbances

Enhanced traffic flow smoothness demonstrated in simulations

Robust control maintains stability under nonlinear conditions

Abstract

Connected and automated vehicles (CAVs) technologies promise to attenuate undesired traffic disturbances. However, in mixed traffic where human-driven vehicles (HDVs) also exist, the nonlinear human-driving behavior has brought critical challenges for effective CAV control. This paper employs the policy iteration method to learn the optimal robust controller for nonlinear mixed traffic systems. Precisely, we consider the H_infty control framework and formulate it as a zero-sum game, the equivalent condition for whose solution is converted into a Hamilton-Jacobi inequality with a Hamiltonian constraint. Then, a policy iteration algorithm is designed to generate stabilizing controllers with desired attenuation performance. Based on the updated robust controller, the attenuation level is further optimized in sum of squares program by leveraging the gap of the Hamiltonian constraint. Simulation studies verify that the obtained controller enables the CAVs to dampen traffic perturbations and smooth traffic flow.