论文标题
事件触发的控制和功率有效资源分配的联合设计的学习方法
A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation
论文作者
论文摘要
在新兴的工业网络物理系统（ICPS）中，由于这些子系统相互联系，交流和控制子系统的联合设计至关重要。在本文中，我们研究了第五代（5G）无线网络中事件触发的控制和节能资源分配的联合设计问题。我们正式将问题称为多目标优化，旨在最大程度地减少执行器输入的更新数量以及下行链路传输中的功耗。为了解决这个问题，我们提出了一种无模型的层次增强学习方法\ TextColor {blue} {具有统一的最终有限性稳定性保证}，同时学习四个政策。这些策略包含有关执行者的输入，控制策略以及节能的子载波和电力分配策略的更新时间策略。我们的仿真结果表明，所提出的方法可以正确控制模拟的ICP，并显着减少执行器输入的更新和下行链路功耗。
In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5G) wireless network. We formally state the problem as a multi-objective optimization one, aiming to minimize the number of updates on the actuators' input and the power consumption in the downlink transmission. To address the problem, we propose a model-free hierarchical reinforcement learning approach \textcolor{blue}{with uniformly ultimate boundedness stability guarantee} that learns four policies simultaneously. These policies contain an update time policy on the actuators' input, a control policy, and energy-efficient sub-carrier and power allocation policies. Our simulation results show that the proposed approach can properly control a simulated ICPS and significantly decrease the number of updates on the actuators' input as well as the downlink power consumption.
