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.

在有限的通信和计算资源下，本研究论文探讨了可扩展无线网络控制系统（WNCS）的控制稳定性的关键设计挑战之一，通过引入一种称为基于尾部控制的替代控制概念，扩展了多动态控制系统在共享无线网络上的经典线性二次调节器（LQR）成本函数。我们将多个控制系统的控制建模为一个网络优化问题，并采用传感器调度、系统状态预测和控制策略来进行解耦。在此基础上，我们提出了一个解决方案，包括基于李雅普诺夫优化的传感器调度算法、基于高斯过程回归（GPR）的状态预测和不确定性估计机制，以及基于强化学习（RL）的控制策略以确保尾部控制的稳定性。我们使用一组离散时不变的山车控制系统来评估所提出的解决方案，并与使用最先进的调度、预测和控制方法的四个变体进行比较。实验结果表明，与最先进的方法相比，该方法在通信和控制资源利用方面总体成本减少了 22%。