Assessing robustness in data-driven modeling of cyber-physical systems

Publication date
2025-05-27
Document type
Konferenzbeitrag
Author
Schmidt, Maximilian
Plambeck, Swantje
Fey, Goerschwin
Organisational unit
Institute of Embedded Systems, Computer Engineering Hamburg University of Technology
DOI
URI
Conference
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2025
First page
1
Last page
11
Is part of
Part of the university bibliography
Nein
Files
Language
English
Keyword
Robustness
Cyber-physical systems
Data-driven modeling
Abstract
Robustness is a key factor in the design and analysis of Cyber-Physical Systems (CPS), ensuring that systems function correctly even under perturbations. This paper investigates robustness within the data-driven modeling process, focusing on three core aspects: system robustness, model robustness, and learner robustness. We survey existing notions of robustness and propose unified formal definitions for each aspect, analyzing their interdependencies and their contributions to overall CPS performance. Additionally, we introduce a method for assessing the robustness of models generated by data-driven learning that is independent of both the model’s internal representation and the learning paradigm used. Our approach leverages input perturbations combined with probabilistic analysis to evaluate how well a learned model handles input variations, particularly when formal guarantees are challenging to obtain. To demonstrate the practical application of our method, we conduct a case study on a temperature control system, using decision trees to model system behavior. By perturbing test data and analyzing the resulting model outputs, we identify non-robust regions near decision boundaries, thereby revealing potential vulnerabilities. The proposed framework offers valuable insights for enhancing system design and lays the groundwork for future research into robust machine learning models for CPS.
Version
Published version
Access right on openHSU
Open access