多目标基于智能体的模型预测控制器用于即插即用车辆控制
Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control
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- University of Waterloo（滑铁卢大学）
- University of Ottawa（渥太华大学）
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中文总结 AI 辅助
本文提出多目标基于智能体的模型预测控制（AMPC），利用ADMM解耦目标并近似全局优化，首次应用于车辆控制，仿真和实车验证表明其灵活且可降低计算成本。
中文摘要 AI 辅助
功能集成是车辆控制中日益增长的趋势，通常涉及多个控制器的协调以同时实现各种目标。对灵活性和可靠性的需求导致了控制系统设计中的“即插即用”方法，这给传统的集成模型预测控制（MPC）带来了挑战。基于智能体的模型预测控制（AMPC）最近作为一种分布式解决方案出现，将控制器视为智能体，在它们之间建立协作框架以达成共同目标。然而，当智能体耦合或相互依赖时，这种方法难以管理分布式冲突目标。为解决这一问题，我们提出了一种新颖、实用的分布式控制方案，称为多目标AMPC，它将交替方向乘子法（ADMM）改编为一种通用控制策略，在解耦目标的同时近似全局优化。我们系统地开发了三种公式，在保持收敛性的同时处理控制正则化和不等式约束，并首次将其应用于复杂的车辆控制系统。所提出的方法已在两个具有多目标拓扑的车辆控制场景中进行了测试。通过仿真比较了不同的公式，并将计算效率最高的公式实现在电动车辆上进行实际评估。结果表明，所提出的多目标AMPC能够近似收敛到与集成MPC相同的全局最优解，同时具有更大的灵活性和降低计算成本的潜力。
英文摘要
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.
