This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of control systems that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only one control system is controlled, while the rest of the control systems are actuated by locally predicting the missing state and/or action information using the previous uplink and/or downlink receptions via a Gaussian process regression (GPR). This GPR prediction credibility is determined using the age-of-information (AoI) of the latest reception. Moreover, the successful reception is affected by the transmission power, mandating a co-design of the communication and control operations. To this end, we formulate a network-wide minimization problem of the average AoI and transmission power under communication reliability and control stability constraints. To solve the problem, we propose a dynamic control algorithm using the Lyapunov drift-plus-penalty optimization framework. Numerical results corroborate that the proposed algorithm can stably control $2\times$ more number of actuators compared to an event-triggered scheduling baseline with Kalman filtering and frequency division multiple access, which is $18\times$ larger than a round-robin scheduling baseline.

中文翻译：

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通过双向高斯过程回归和 AoI 感知调度的预测控制和通信协同设计

本文研究了上下行调度和功率分配的联合问题，以控制大量控制系统，这些控制系统将其状态上传到遥控器并通过无线链路下载控制动作。为了克服无线资源的缺乏，我们提出了一种基于机器学习的解决方案，其中只控制一个控制系统，而其余的控制系统通过使用先前的上行链路和/或动作信息在本地预测丢失的状态和/或动作信息来驱动。 / 或通过高斯过程回归 (GPR) 的下行链路接收。该 GPR 预测可信度是使用最新接收的信息年龄 (AoI) 确定的。此外，成功接收受传输功率的影响，要求通信和控制操作的协同设计。为此，我们在通信可靠性和控制稳定性约束下制定了平均 AoI 和传输功率的网络范围最小化问题。为了解决这个问题，我们提出了一种使用李雅普诺夫漂移加惩罚优化框架的动态控制算法。数值结果证实了所提出的算法可以稳定控制 $2\times$ 与具有卡尔曼滤波和频分多址访问的事件触发调度基线相比，执行器的数量更多，这是 $18\次$ 大于循环调度基线。