# Performance Characterization Using AoI in a Single-loop Networked Control System

## TL;DR

This paper explores how Age-of-Information (AoI) can be used to optimize control and estimation in a networked control system with random transmission times, proposing heuristic algorithms for minimizing estimation error.

## Contribution

It demonstrates that minimizing AoI can effectively reduce estimation error in NCS and provides a framework for joint sampling and scheduling design under complex communication models.

## Key findings

- Optimal control can be designed independently from sampling under mild assumptions.
- Minimizing AoI is equivalent to minimizing estimation error for certain system classes.
- Heuristic algorithms extend AoI literature to control system optimization.

## Abstract

The joint design of control and communication scheduling in a Networked Control System (NCS) is known to be a hard problem. Several research works have successfully designed optimal sampling and/or control strategies under simplified communication models, where transmission delays/times are negligible or fixed. However, considering sophisticated communication models, with random transmission times, result in highly coupled and difficult-to-solve optimal design problems due to the parameter inter-dependencies between estimation/control and communication layers. To tackle this problem, in this work, we investigate the applicability of Age-of-Information (AoI) for solving control/estimation problems in an NCS under i.i.d. transmission times. Our motivation for this investigation stems from the following facts: 1) recent results indicate that AoI can be tackled under relatively sophisticated communication models, and 2) a lower AoI in an NCS may result in a lower estimation/control cost. We study a joint optimization of sampling and scheduling for a single-loop stochastic LTI networked system with the objective of minimizing the time-average squared norm of the estimation error. We first show that under mild assumptions on information structure the optimal control policy can be designed independently from the sampling and scheduling policies. We then derive a key result that minimizing the estimation error is equivalent to minimizing a function of AoI when the sampling decisions are independent of the state of the LTI system. Noting that minimizing the function of AoI is a stochastic combinatorial optimization problem and is hard to solve, we resort to heuristic algorithms obtained by extending existing algorithms in the AoI literature. We also identify a class of LTI system dynamics for which minimizing the estimation error is equivalent to minimizing the expected AoI.