发表机构
Technical University of Munich; ETH Zurich; Eindhoven University of Technology; East China University of Science and Technology; Munich Institute of Robotics and Machine Intelligence（慕尼黑工业大学; 苏黎世联邦理工学院; 埃因霍温理工大学; 华东理工大学; 慕尼黑机器人与机器智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对存在计算延迟的网络化系统，提出结合异步事件触发机制的在线学习控制框架，推导跟踪误差界与事件触发条件，实现与时间触发相当的控制性能并排除芝诺行为。
AI 中文摘要
基于在线学习的控制是控制不确定系统的一种有前景的方法，在运行过程中识别未知组件以提升控制性能。然而，资源密集型的在线学习算法会引入不可忽视的计算延迟，尤其是在本地计算资源有限的系统上执行时。为缓解该问题，采用网络内在线学习控制结构，将基于学习的控制器部署在远程计算节点，并通过通信通道连接。本文首先推导考虑计算延迟的网络内控制架构的跟踪误差界，建立控制性能保证；该跟踪误差界允许特定条件下的多种通信与计算策略，包括时间/事件触发机制。此外，针对给定的期望控制性能，展示通信与计算性能间的权衡关系。进一步，为提升通信与计算效率，在存在计算延迟的情况下，设计一种控制与在线学习均采用异步事件触发机制的高效控制框架。所提出的事件触发策略被证明可达到与时间触发场景相同的控制性能，同时排除芝诺行为。最后，针对可指数稳定的系统，推导所提事件触发条件的显式表达式，并通过仿真验证其有效性。
英文摘要
Online learning-based control is a promising approach to control uncertain systems, where unknown components are identified during operation to improve control performance. However, resource-intensive online learning algorithms introduce non-negligible computational delays, especially when executed on systems with limited local computational resources. To mitigate this, an in-network online learning-based control structure is employed by deploying the learning-based controller on a remote computation node and connecting it via a communication channel. In this paper, control performance guarantee is first established by deriving tracking error bound for the in-network control architecture, while accounting for computational delays. The derived tracking error bound allows for diverse communication and computation strategies under a specific condition, including time-/event-triggered mechanisms. Additionally, the trade-off between communication and computation performances is shown for a given desired control performance. Furthermore, to enhance the efficiency in both communication and computation, an efficient control framework with an asynchronous event-triggered mechanism in both control and online learning is devised under the existence of computational delay. The proposed event-triggered strategy is proven to achieve the same control performance as time-triggered scenario while excluding Zeno behavior. Finally, we derive an explicit expression of the proposed event-trigger condition for exponentially stabilizable systems, and demonstrate its effectiveness through simulations.
