This paper introduces a novel data-driven hierarchical control scheme for managing a fleet of nonlinear, capacity-constrained autonomous agents in an iterative environment. We propose a control framework consisting of a high-level dynamic task assignment and routing layer and low-level motion planning and tracking layer. Each layer of the control hierarchy uses a data-driven Model Predictive Control (MPC) policy, maintaining bounded computational complexity at each calculation of a new task assignment or actuation input. We utilize collected data to iteratively refine estimates of agent capacity usage, and update MPC policy parameters accordingly. Our approach leverages tools from iterative learning control to integrate learning at both levels of the hierarchy, and coordinates learning between levels in order to maintain closed-loop feasibility and performance improvement of the connected architecture.

翻译：本文提出了一种新颖的数据驱动分层控制方案，用于在迭代环境中管理由非线性、容量约束的自主智能体组成的集群。我们设计了一个包含高层动态任务分配与路由层以及低层运动规划与跟踪层的控制框架。控制层次中的每一层均采用数据驱动模型预测控制（MPC）策略，在每次计算新任务分配或执行输入时保持计算复杂度有界。我们利用收集的数据迭代精化智能体容量使用的估计值，并据此更新MPC策略参数。本方法借助迭代学习控制工具，在控制层级的两层中集成学习，并通过协调层间学习，维持闭环可行性并提升互联架构的性能表现。