Predictor-Feedback CACC for Vehicular Platoons with Actuation and Communication Delays Based on a Multiple-Predecessor-Following CTH Nominal Strategy

TL;DR

This paper introduces a predictor-feedback CACC method for vehicle platoons that compensates for actuation and communication delays, ensuring stability and improved traffic flow.

Contribution

It presents a novel MPF topology-based delay compensation strategy with explicit stability conditions, validated through simulations including real-world data.

Findings

Achieves stability and zero steady-state errors despite delays

Provides explicit string stability conditions based on model parameters

Demonstrates improved traffic throughput in simulations

Abstract

We develop a predictor-feedback cooperative adaptive cruise control (CACC) design relying on a multiple-predecessor-following (MPF) topology-based nominal delay-free CACC law. We consider vehicular platoons with heterogeneous vehicles, whose dynamics are described by a third-order linear system subject to actuation delay, along with vehicle-to-vehicle (V2V) communication delay. The design achieves individual vehicle stability, string stability, and zero, steady-state speed/spacing tracking errors, for any value of the actuation delay. The proofs of individual vehicle stability, string stability, and regulation rely on employment of an input-output approach on the frequency domain, capitalizing on the delay-compensating property of the design, which enables as to derive explicit string stability conditions on control and vehicle models parameters. The theoretical guarantees of string stability and the respective conditions on parameters are illustrated also numerically. We present consistent simulation results, for a ten-vehicle platoon, illustrating the potential of the design in traffic throughput improvement, as compared with a predictor-feedback CACC design in which, each ego vehicle's controller utilizes information only from a single preceding vehicle. We also present simulation results in a realistic scenario in which the leading vehicle's trajectory is obtained from NGSIM data.