Distributed Observers-based Cooperative Platooning Tracking Control and Intermittent Optimization for Connected Automated Vehicles with Unknown Jerk Dynamics

TL;DR

This paper develops distributed observer-based control and intermittent optimization methods for connected automated vehicle platoons, addressing unknown jerk dynamics to improve safety, efficiency, and robustness in vehicle tracking.

Contribution

It introduces a novel distributed observer law and control protocol for platooning with unknown jerk, along with an intermittent optimization scheme for feedback gain scheduling.

Findings

Effective tracking control under unknown jerk demonstrated in simulations.

Robust intermittent optimization reduces control costs and enhances safety.

Distributed approach enables local information use for cooperative vehicle control.

Abstract

The unknown sharp changes of vehicle acceleration rates, also called the unknown jerk dynamics, may significantly affect the driving performance of the leader vehicle in a platoon, resulting in more drastic car-following movements in platooning tracking control, which could cause safety and traffic capacity concerns. To address these issues, in this paper, we investigate cooperative platooning tracking control and intermittent optimization problems for connected automated vehicles (CAVs) with a nonlinear car-following model. We assume that the external inputs of the leader CAV contain unknown but bounded jerk parameters, and the acceleration signals of the leader CAV are known only to a few neighboring follower CAVs in a free-design but directed communication network. To solve these problems, a distributed observer law is developed to provide a reference signal expressed as an estimated unknown jerk dynamic of the leader CAV and implemented by each follower CAV. Then, a novel distributed platooning tracking control protocol is proposed to construct the cooperative tracking controllers under identical inter-vehicle constraints, which can ensure a desired safety distance among the CAVs and allow each follower CAV to track their leader CAV by using only local information interaction. We also present a novel intermittent sampling condition and a robust intermittent optimization design that can ensure optimally scheduled feedback gains for the cooperative platooning tracking controllers to minimize the control cost under nonidentical inter-vehicle constraints and unknown jerk dynamics. Simulation case studies are carried out to illustrate the effectiveness of the proposed approaches