Input-Output Data-Driven Sensor Selection for Cyber-Physical Systems

TL;DR

This paper introduces a model-free, data-driven method for sensor selection in cyber-physical systems that maximizes observability metrics related to system energy, using reinforcement learning concepts and polynomial-time algorithms.

Contribution

It develops a novel input-output data-driven algorithm for sensor selection that does not require system model knowledge and guarantees convergence.

Findings

The method effectively selects sensors to maximize observability.

The approach is computationally efficient with polynomial-time complexity.

Simulations confirm the method's validity and effectiveness.

Abstract

In this paper, we consider the problem of input-output data-driven sensor selection for unknown cyber-physical systems (CPS). In particular, out of a large set of sensors available for use, we choose a subset of them that maximizes a metric of observability of the CPS. The considered observability metric is related to the $\mathcal{H}_2$ system norm, which quantifies the average output energy of the selected sensors over a finite or an infinite horizon. However, its computation inherently requires knowledge of the unknown matrices of the system, so we draw connections from the reinforcement learning literature and design an input-output data-driven algorithm to compute it in a model-free manner. We then use the derived data-driven metric expression to choose the best sensors of the system in polynomial time, effectively obtaining a provably convergent model-free sensor selection process. Additionally, we show how the proposed data-driven approach can be exploited to select sensors that optimize volumetric measures of observability, while also noting its applicability to the dual problem of actuator selection. Simulations are performed to demonstrate the validity and effectiveness of the proposed approach.