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This research introduces Learning Agent-based Model Predictive Control (LAMPC), a hybrid framework that integrates Gaussian process regression with traditional model predictive control to optimize multi-agent vehicle systems. By leveraging data-driven learning to predict unknown agent contributions, the method improves tracking performance while maintaining safety constraints even in scenarios with limited prior knowledge. The approach demonstrates significant practical value for autonomous driving and coordinated robotics by bridging the gap between idealized theoretical models and real-world implementation challenges.
Learning Agent-based Model Predictive Control for Holistic Vehicle Performance
arXiv:2609.11871v1 Announce Type: new Abstract: Agent-based model predictive control (AMPC) has recently been proposed as a distributed scheme that collaborates with all agents to achieve optimal holistic performance. However, its optimality highly depends on the prediction accuracy that requires all agents or their contributions to be known, which is too idealistic for actual implementation. This research proposes a novel practical hybrid control scheme - learning agent-based MPC (LAMPC), combining the model-based AMPC approach and data-based learning methods to improve the holistic vehicle performance for multi-agent systems. The Gaussian process regression (GPR) enhanced by an online data management strategy serves as the learning core to predict unknown contributions. A novel multi-step prediction mechanism leverages the GPR learning potential along the horizon. The predicted mean, representing the learned unknown contributions, completes the system model in the MPC for more accurate control. Meanwhile, a stochastic framework is formulated to guarantee control safety and feasibility using soft chance constraints based on the prediction variance. Both simulations and experiments show that, with the learning capability, LAMPC outperforms the traditional AMPC. LAMPC can achieve higher tracking performance in well-learned scenarios and always guarantee constraint satisfaction even in less-learned scenarios. Moreover, the proposed hybrid control scheme is efficient for real-time implementation and is flexible to any control agent topology.
Abstract:Agent-based model predictive control (AMPC) has recently been proposed as a distributed scheme that collaborates with all agents to achieve optimal holistic performance. However, its optimality highly depends on the prediction accuracy that requires all agents or their contributions to be known, which is too idealistic for actual implementation. This research proposes a novel practical hybrid control scheme - learning agent-based MPC (LAMPC), combining the model-based AMPC approach and data-based learning methods to improve the holistic vehicle performance for multi-agent systems. The Gaussian process regression (GPR) enhanced by an online data management strategy serves as the learning core to predict unknown contributions. A novel multi-step prediction mechanism leverages the GPR learning potential along the horizon. The predicted mean, representing the learned unknown contributions, completes the system model in the MPC for more accurate control. Meanwhile, a stochastic framework is formulated to guarantee control safety and feasibility using soft chance constraints based on the prediction variance. Both simulations and experiments show that, with the learning capability, LAMPC outperforms the traditional AMPC. LAMPC can achieve higher tracking performance in well-learned scenarios and always guarantee constraint satisfaction even in less-learned scenarios. Moreover, the proposed hybrid control scheme is efficient for real-time implementation and is flexible to any control agent topology.
12 pages. Author accepted manuscript
Robotics (cs.RO)
arXiv:2609.11871 [cs.RO]
(or arXiv:2609.11871v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.11871 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 11, pp. 17482-17492, November 2024
https://doi.org/10.1109/TITS.2024.3435551 Focus to learn more DOI(s) linking to related resources
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