AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
[Submitted on 10 Sep 2026] Title:Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control View a PDF of the paper titled Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control, by Jiaming Zhong and 5 other authors View PDF HTML (experimental) 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. Comments: 12 pages, 13 figures. Author accepted manuscript Subjects: Robotics (cs.RO) Cite as: arXiv:2609.12108 [cs.RO] (or arXiv:2609.12108v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.12108 arXiv-issued DOI via DataCite (pending registration) Journal reference: IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 12, pp. 22818-22829, December 2025 Related DOI: https://doi.org/10.1109/TITS.2025.3612984 DOI(s) linking to related resources Submission history From: Jiaming Zhong [view email] [v1] Thu, 10 Sep 2026 18:35:08 UTC (4,973 KB) Full-text links: Access Paper: View a PDF of the paper titled Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control, by Jiaming Zhong and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)