Multi-Objective Following Control for Heavy-Duty Vehicles using Differential Dynamic Programming

摘要

The speed with which an ego-vehicle follows a lead vehicle through traffic can significantly affect the former’s fuel consumption, safety, average speed, and ride comfort. This paper merges these objectives and constraints into a unified trajectory optimization problem. One of the paper’s goals is to provide a unified formulation of problems traditionally tackled independently, e.g., platooning, fuel-minimizing vehicle speed trajectory optimization, etc. Another key goal is to demonstrate the degree to which Differential Dynamic Programming (DDP) provides a conceptually attractive and computationally inexpensive decomposition of the resulting multi-objective problem. In this decomposition, perturbations from an optimal steady-state vehicle trajectory are controlled using a linear quadratic regulation (LQR) law obtained analytically through DDP. We examine the performance of this controller simulating a representative urban drive cycle with a lead vehicle. We also perform a sensitivity study on the parameters in the objective and explore their effect in both fuel economy and deviations from nominal headway distance. Finally, we explore the effect of different levels of collaboration between vehicles by assuming the lead vehicle shares its predicted future average acceleration.