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 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、容量受限的自主代理组成的编队。我们构建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的模型预测控制策略，在每次计算新任务分配或驱动输入时保持有界计算复杂度。我们利用收集的数据迭代优化代理容量使用估计，并据此更新模型预测控制策略参数。该方法借助迭代学习控制工具，在层次结构的两个层级上整合学习能力，并通过协调层级间的学习来维持闭环可行性，同时提升互联架构的性能表现。