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A Comparison Benchmark for Distributed Hybrid MPC Control Methods: Distributed Vehicle Platooning

Samuel Mallick, Azita Dabiri, Bart De Schutter

TL;DR

This work introduces a standardized benchmark for evaluating distributed hybrid MPC on a platoon of gear-managed autonomous vehicles. It provides two hybrid vehicle models (a PWA gear model and a discrete-input gear model), five MPC controllers (centralized, decentralized, sequential, event-based, and ADMM-based), and an open-source codebase for reproducible comparisons. Through three tasks with varying platoon sizes, references, and spacing policies, the study reveals trade-offs between tracking performance and computational burden, highlighting the computational challenges of solving mixed-integer problems online in large-scale hybrid networks. The results motivate the development of new distributed hybrid MPC methods that can approximate centralized performance with lower online complexity while offering theoretical guarantees on feasibility and stability in practical platooning scenarios.

Abstract

Distributed model predictive control (MPC) is currently being investigated as a solution to the important control challenge presented by networks of hybrid dynamical systems. However, a benchmark problem for distributed hybrid MPC is absent from the literature. We propose distributed control of a platoon of autonomous vehicles as a comparison benchmark problem. The problem provides a complex and adaptable case study, upon which existing and future approaches to distributed MPC for hybrid systems can be evaluated. Two hybrid modeling frameworks are presented for the vehicle dynamics. Five hybrid MPC controllers are then evaluated and extensively assessed on the fleet of vehicles. Finally, we comment on the need for new efficient and high performing distributed MPC schemes for hybrid systems.

A Comparison Benchmark for Distributed Hybrid MPC Control Methods: Distributed Vehicle Platooning

TL;DR

This work introduces a standardized benchmark for evaluating distributed hybrid MPC on a platoon of gear-managed autonomous vehicles. It provides two hybrid vehicle models (a PWA gear model and a discrete-input gear model), five MPC controllers (centralized, decentralized, sequential, event-based, and ADMM-based), and an open-source codebase for reproducible comparisons. Through three tasks with varying platoon sizes, references, and spacing policies, the study reveals trade-offs between tracking performance and computational burden, highlighting the computational challenges of solving mixed-integer problems online in large-scale hybrid networks. The results motivate the development of new distributed hybrid MPC methods that can approximate centralized performance with lower online complexity while offering theoretical guarantees on feasibility and stability in practical platooning scenarios.

Abstract

Distributed model predictive control (MPC) is currently being investigated as a solution to the important control challenge presented by networks of hybrid dynamical systems. However, a benchmark problem for distributed hybrid MPC is absent from the literature. We propose distributed control of a platoon of autonomous vehicles as a comparison benchmark problem. The problem provides a complex and adaptable case study, upon which existing and future approaches to distributed MPC for hybrid systems can be evaluated. Two hybrid modeling frameworks are presented for the vehicle dynamics. Five hybrid MPC controllers are then evaluated and extensively assessed on the fleet of vehicles. Finally, we comment on the need for new efficient and high performing distributed MPC schemes for hybrid systems.
Paper Structure (24 sections, 24 equations, 11 figures, 3 tables)

This paper contains 24 sections, 24 equations, 11 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Vehicle Models
  3. Models
  4. PWA gear model
  5. Discrete-input gear model
  6. The Benchmark Problem
  7. Constraints
  8. Optimal Control Problem
  9. Feasibility
  10. Performance Measures
  11. Open Source Code
  12. Hybrid MPC Controllers
  13. Centralized
  14. Decentralized MPC
  15. Sequential distributed MPC
  16. ...and 9 more sections

Figures (11)

  • Figure 1: PWA friction approximation. True quadratic function (solid), piecewise approximation (dashed).
  • Figure 2: Gear models.
  • Figure 3: Platoon control.
  • Figure 4: Platoon formation under the centralized controller for the 3 tasks with $M = 3$ and $N = 6$. , Position (top), inter-vehicle spacing (middle) with safe distance (red line), and velocity (bottom).
  • Figure 5: Platoon formation under the centralized controller with 1- and 2-norm costs for $M = 5$ and $N = 6$. Position (top), inter-vehicle spacing (middle) with safe distance (red line), and velocity (bottom).
  • ...and 6 more figures

Theorems & Definitions (3)

  • Remark 1
  • Remark 2
  • Remark 3