Resource Optimization for Tail-Based Control in Wireless Networked Control Systems

TL;DR

This paper introduces a novel resource optimization framework for tail-based control in wireless networked control systems, combining scheduling, prediction, and reinforcement learning to improve stability and reduce resource use.

Contribution

It proposes an integrated solution using Lyapunov-based scheduling, Gaussian Process Regression for prediction, and RL control policies for tail-based stability in WNCS.

Findings

22% reduction in communication and control resource utilization

Outperforms four state-of-the-art methods in experiments

Enhances control stability under limited resources

Abstract

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.