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

• URL: http://arxiv.org/abs/2609.12108v1
• Date: Thu, 10 Sep 2026 18:35:08 GMT
• Title: Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control
• Abstract summary: Functional integration is a growing trend in vehicle control, often involving the coordination of multiple controllers.<n>Agent-based model predictive control (AMPC) has emerged as a distributed solution that treats controllers as agents.<n>We propose a novel, practical distributed control scheme called multi-objective AMPC.
• Score: 18.92447175833456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Data-Driven Distributed Control Scheme: Learning Multi-Objective Agent-Based MPC for Path-Tracking [16.049627875354503]
  Agent-based model predictive control (AMPC) has recently been proposed for vehicle systems with various controllers.<n>This paper proposes a learning multi-objective AMPC that can improve design flexibility and computing efficiency.
  arXiv  Detail & Related papers  (2026-09-10T19:22:52Z)
• MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks [71.01350912967922]
  In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.<n>This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services.<n>We develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-08-02T10:05:50Z)
• Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs [31.34021188099265]
  Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models.<n>We propose a critique-and-routing controller that casts multi-agent coordination as a sequential decision problem.
  arXiv  Detail & Related papers  (2026-05-09T04:51:42Z)
• PCHC: Enabling Preference Conditioned Humanoid Control via Multi-Objective Reinforcement Learning [50.63196995993855]
  We propose a novel framework leveraging Multi-Objective Reinforcement Learning (MORL) to achieve Preference-Conditioned Humanoid Control (PCHC)<n>Our framework enables a single, preference-conditioned policy to exhibit a wide spectrum of diverse behaviors.
  arXiv  Detail & Related papers  (2026-03-25T07:55:37Z)
• MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning [68.91090643731987]
  Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems.<n>Existing approaches are limited to separate fields and can only handle multi-agent decision-making with a single objective.<n>We propose MO-mix to solve the multi-objective multi-agent reinforcement learning (MOMARL) problem.
  arXiv  Detail & Related papers  (2026-02-28T16:25:22Z)
• Collab: Controlled Decoding using Mixture of Agents for LLM Alignment [90.6117569025754]
  Reinforcement learning from human feedback has emerged as an effective technique to align Large Language models.<n>Controlled Decoding provides a mechanism for aligning a model at inference time without retraining.<n>We propose a mixture of agent-based decoding strategies leveraging the existing off-the-shelf aligned LLM policies.
  arXiv  Detail & Related papers  (2025-03-27T17:34:25Z)
• Reinforcement Learning with Model Predictive Control for Highway Ramp Metering [14.389086937116582]
  This work explores the synergy between model-based and learning-based strategies to enhance traffic flow management.<n>The control problem is formulated as an RL task by crafting a suitable stage cost function.<n>An MPC-based RL approach, which leverages the MPC optimal problem as a function approximation for the RL algorithm, is proposed to learn to efficiently control an on-ramp.
  arXiv  Detail & Related papers  (2023-11-15T09:50:54Z)
• Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning [9.939081691797858]
  We present a reinforcement learning (RL) approach for the multi-agent efficient domain coverage problem involving agents with second-order dynamics. Our proposed network architecture includes the incorporation of LSTM and self-attention, which allows the trained policy to adapt to a variable number of agents.
  arXiv  Detail & Related papers  (2022-11-11T01:59:12Z)
• Policy Search for Model Predictive Control with Application to Agile Drone Flight [56.24908013905407]
  We propose a policy-search-for-model-predictive-control framework for MPC. Specifically, we formulate the MPC as a parameterized controller, where the hard-to-optimize decision variables are represented as high-level policies. Experiments show that our controller achieves robust and real-time control performance in both simulation and the real world.
  arXiv  Detail & Related papers  (2021-12-07T17:39:24Z)
• Imitation Learning from MPC for Quadrupedal Multi-Gait Control [63.617157490920505]
  We present a learning algorithm for training a single policy that imitates multiple gaits of a walking robot. We use and extend MPC-Net, which is an Imitation Learning approach guided by Model Predictive Control. We validate our approach on hardware and show that a single learned policy can replace its teacher to control multiple gaits.
  arXiv  Detail & Related papers  (2021-03-26T08:48:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.