论文标题
数据驱动的分层预测学习在未知环境中
Data-Driven Hierarchical Predictive Learning in Unknown Environments
论文作者
论文摘要
我们为未知环境中的预测控制提出了一个分层学习体系结构。我们考虑一个受约束的非线性动力学系统，并假设在不同环境中解决控制控制任务的状态输入轨迹的可用性。一个参数化的环境模型生成了针对每个任务的状态约束，而存储轨迹满足。我们的目标是在未知环境中找到一项新任务的可行轨迹。从存储的数据中，我们以减少订单状态空间中目标集的形式学习策略。这些策略使用新环境的局部预测将实时的新任务应用于新任务，并且所得的输出通过低级后退的地平线控制器用作终端区域。我们展示了如何）设计从过去数据中设计目标集，然后ii）将它们纳入模型预测控制方案中，并使用转换范围，以确保执行新任务时闭环系统的安全性。我们证明了由此产生的控制政策的可行性，并在机器人路径计划应用中验证了所提出的方法。
We propose a hierarchical learning architecture for predictive control in unknown environments. We consider a constrained nonlinear dynamical system and assume the availability of state-input trajectories solving control tasks in different environments. A parameterized environment model generates state constraints specific to each task, which are satisfied by the stored trajectories. Our goal is to find a feasible trajectory for a new task in an unknown environment. From stored data, we learn strategies in the form of target sets in a reduced-order state space. These strategies are applied to the new task in real-time using a local forecast of the new environment, and the resulting output is used as a terminal region by a low-level receding horizon controller. We show how to i) design the target sets from past data and then ii) incorporate them into a model predictive control scheme with shifting horizon that ensures safety of the closed-loop system when performing the new task. We prove the feasibility of the resulting control policy, and verify the proposed method in a robotic path planning application.
