发表机构
University of Waterloo; University of Ottawa; General Motors Company（滑铁卢大学; 渥太华大学; 通用汽车公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出学习型智能体模型预测控制（LAMPC），结合模型与数据方法，用高斯过程回归预测未知贡献，提升多智能体系统整车性能，仿真实验优于传统AMPC。
AI 中文摘要
智能体模型预测控制（AMPC）最近被提出作为一种分布式方案，与所有智能体协作以实现最优的整体性能。然而，其最优性高度依赖于预测精度，这要求所有智能体或其贡献已知，这对于实际实施而言过于理想化。本研究提出了一种新颖的实用混合控制方案——基于学习的智能体MPC（LAMPC），将基于模型的AMPC方法与基于数据的学习方法相结合，以提高多智能体系统的整车性能。由在线数据管理策略增强的高斯过程回归（GPR）作为学习核心，用于预测未知贡献。一种新颖的多步预测机制沿预测时域充分利用GPR的学习潜力。预测均值代表学习到的未知贡献，完善了MPC中的系统模型，以实现更精确的控制。同时，构建了一个随机框架，基于预测方差使用软机会约束来保证控制安全性和可行性。仿真和实验均表明，凭借学习能力，LAMPC优于传统AMPC。LAMPC在充分学习的场景中能够实现更高的跟踪性能，并且在学习较少的场景中也能始终保证约束满足。此外，所提出的混合控制方案对于实时实施是高效的，并且适用于任何控制智能体拓扑。
英文摘要
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.
Comments12 pages. Author accepted manuscript
Journal refIEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 11, pp. 17482-17492, November 2024
