发表机构
University of Waterloo（滑铁卢大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种数据驱动的学习多目标基于智能体的模型预测控制方案，通过ADMM解耦和基于学习的迭代初始化，在保持与集成方案相同控制性能的同时，显著降低计算时间，适用于自动驾驶车辆路径跟踪。
AI 中文摘要
基于智能体的模型预测控制（AMPC）最近被提出用于具有各种控制器（如差动制动和扭矩矢量分配）的车辆系统，其中控制器被视为分布式智能体，共同为同一目标做出贡献。然而，该方案在处理具有耦合智能体的多个冲突目标时面临挑战。此类任务的常见方法是集成MPC，即将所有目标和智能体堆叠在一个优化问题中。然而，随着智能体和目标数量的增加，集成MPC在实践中将面临计算负担和维护困难等挑战。为此，本文提出了一种学习多目标AMPC，以提高设计灵活性和计算效率。首先，在信息交换的假设下，提出了一种基于交替方向乘子法（ADMM）定制的多目标AMPC，以解耦系统并迭代地实现与集成方案相同的性能。其次，提出了一种基于学习的迭代初始化方法以加速收敛。此外，提出了一种数据管理方法以实现实时效率，并设计了一个认证模块以确保学习可靠性。我们通过结合多种控制器的自动驾驶车辆路径跟踪仿真，将所提出的方案与集成方案进行了比较。所提出的方案在实现与集成方案相同控制性能的同时，将计算时间减少了43.5%。此外，基于学习的方法比无学习方法节省了88.6%的计算时间，使其适用于实时实现。
英文摘要
Agent-based model predictive control (AMPC) has recently been proposed for vehicle systems with various controllers, such as differential braking and torque vectoring, where controllers are regarded as distributed agents contributing to the same objective. However, this scheme is challenging in handling multiple conflicting objectives with coupled agents. A common approach for such tasks is the integrated MPC, where all objectives and agents are stacked together in one optimization. Nevertheless, as more agents and objectives are involved, the integrated MPC will face challenges like computational burdens and maintenance difficulties in practice. To this end, this paper proposes a learning multi-objective AMPC that can improve design flexibility and computing efficiency. First, under the assumption of information exchange, a multi-objective AMPC tailored from the alternating direction method of multipliers (ADMM) is proposed to decouple the system and achieve the same performance as the integrated scheme iteratively. Second, a learning-based method for initializing iterations is proposed to accelerate convergence. In addition, a data management method is proposed for real-time efficiency, and an authentication module is designed for learning reliability. We compare the proposed scheme against the integrated scheme via a combined path-tracking simulation for autonomous vehicles with various controllers. The proposed scheme achieves the same control performance as the integrated one while reducing the computational time by 43.5%. Furthermore, the learning-based method saves 88.6% more computational time than without learning, making it suitable for real-time implementation.
Comments7 pages, 8 figures, 2 tables. Author accepted manuscript of a paper published in IEEE ITSC 2024
Journal ref2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Edmonton, AB, Canada, pp. 2999-3004, 2024
DOI：10.1109/ITSC58415.2024.10919711
