aiqbench analysis
This research advances distributed control theory by integrating agent-based model predictive control (AMPC) with the alternating direction method of multipliers to manage conflicting objectives in vehicle systems. The study demonstrates that this approach can approximate global optimization while offering greater flexibility and reduced computational costs compared to traditional integrated methods. Although implemented on an electric vehicle, the core contribution lies in the algorithmic framework for coordinating multiple controllers rather than the vehicle hardware itself.
Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control
arXiv:2609.12108v1 Announce Type: new 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.
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.
12 pages, 13 figures. Author accepted manuscript
Robotics (cs.RO)
arXiv:2609.12108 [cs.RO]
(or arXiv:2609.12108v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.12108 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 12, pp. 22818-22829, December 2025
https://doi.org/10.1109/TITS.2025.3612984 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 340 words
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