Achieving control stability is one of the key design challenges of scalable Wireless Networked Control Systems (WNCS) under limited communication and computing resources. This paper explores the use of an alternative control concept defined as tail-based control, which extends the classical Linear Quadratic Regulator (LQR) cost function for multiple dynamic control systems over a shared wireless network. We cast the control of multiple control systems as a network-wide optimization problem and decouple it in terms of sensor scheduling, plant state prediction, and control policies. Toward this, we propose a solution consisting of a scheduling algorithm based on Lyapunov optimization for sensing, a mechanism based on Gaussian Process Regression (GPR) for state prediction and uncertainty estimation, and a control policy based on Reinforcement Learning (RL) to ensure tail-based control stability. A set of discrete time-invariant mountain car control systems is used to evaluate the proposed solution and is compared against four variants that use state-of-the-art scheduling, prediction, and control methods. The experimental results indicate that the proposed method yields 22% reduction in overall cost in terms of communication and control resource utilization compared to state-of-the-art methods.

翻译：在有限的通信和计算资源下实现控制稳定性，是可扩展无线网络控制系统（WNCS）面临的关键设计挑战之一。本文探讨了一种替代性控制概念——尾部控制的应用，该概念将经典线性二次调节器（LQR）成本函数扩展至共享无线网络上的多个动态控制系统。我们将多个控制系统的控制问题构建为一个网络范围的优化问题，并将其解耦为传感器调度、被控对象状态预测和控制策略三个部分。为此，我们提出了一种解决方案，该方案包含：一种基于李雅普诺夫优化的传感调度算法、一种基于高斯过程回归（GPR）的状态预测与不确定性估计机制，以及一种基于强化学习（RL）的、用于确保尾部控制稳定性的控制策略。我们使用一组离散时不变山地车控制系统对所提方案进行评估，并与采用前沿调度、预测和控制方法的四种变体进行了比较。实验结果表明，与前沿方法相比，所提方法在通信和控制资源利用方面的总体成本降低了22%。