Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control

2026-09-10

AI Summary

为解决插件式车辆控制中多目标冲突问题，提出了一种基于多目标代理模型预测控制的方法，通过交替方向乘子法实现全局优化并解耦目标。

Abstract

Functional integration is a growing trend in vehicle control, often involving the coordination of multiple controllers to achieve various objectives simultaneously. The need for flexibility and reliability has led to a "plug-and-play" approach in control system design, which presents challenges for traditional integrated model predictive control (MPC). Agent-based model predictive control (AMPC) has recently emerged as a distributed solution that treats controllers as agents, creating a collaborative framework among them to reach a common goal. However, this approach struggles to manage distributed conflicting objectives when agents are coupled or interdependent. To address this, we propose a novel, practical distributed control scheme called multi-objective AMPC, which adapts the alternating direction method of multipliers (ADMM) into a general control strategy that approximates global optimization while decoupling objectives. We systematically develop three formulations that maintain convergence while addressing control regularization and inequality constraints, applying them to complex vehicle control systems for the first time. The proposed method has been tested on two vehicle control scenarios with a multi-objective topology. Different formulations are compared through simulations, and the most computationally efficient one was implemented on an electric vehicle for real-world evaluations. The results demonstrate that the proposed multi-objective AMPC can converge approximately to the same global optimum as integrated MPC with greater flexibility and the potential to reduce computational costs.

Problem

• Multi-Objective
• Agent-Based Model Predictive Control
• Plug-and-Play
• Vehicle Control
• Distributed Control

Innovation

• multi-objective AMPC
• ADMM
• distributed control
• vehicle control systems
• global optimization