事件触发脉冲控制与模型学习（如果需要）
Event-triggered Pulse Control with Model Learning (if Necessary)
AI总结：
本文提出了一种新型事件触发脉冲控制策略，能够在需要时学习动态模型，同时适应动态变化并替代传统周期控制中的积分部分。
摘要：
在网络化控制系统中，通信是一种共享且稀缺的资源。事件触发控制（ETC）可以通过显著减少样本数量来实现高性能控制，相比传统的周期性控制方案。然而，ETC方法通常依赖于准确的动力学模型的可用性，这往往不易获得。在本文中，我们提出了一种新颖的事件触发脉冲控制策略，能够在需要时学习动态模型。除了适应变化的动力学外，该方法还代表了传统周期控制中积分部分的合适替代品。
英文摘要：
In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduced amount of samples compared to classical, periodic control schemes. However, ETC methods usually rely on the availability of an accurate dynamics model, which is oftentimes not readily available. In this paper, we propose a novel event-triggered pulse control strategy that learns dynamics models if necessary. In addition to adapting to changing dynamics, the method also represents a suitable replacement for the integral part typically used in periodic control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
