# An Empirical Study of Architectural Shift from Traditional to AI-Enabled Simulink Controllers

arXiv: https://arxiv.org/abs/2609.38504 · HTML: https://mlsanity.com/paper/2609.38504/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2004.05519/) (2020)
- [Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks](https://mlsanity.com/paper/1708.03322/) (2017)
- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward](https://mlsanity.com/paper/2111.04324/) (2021)
- [Mosaic: Model-based Safety Analysis Framework for AI-enabled Cyber-Physical Systems](https://mlsanity.com/paper/2305.03882/) (2023)
- [AutoRepair: Automated Repair for AI-Enabled Cyber-Physical Systems under Safety-Critical Conditions](https://mlsanity.com/paper/2304.05617/) (2023)
- [Evaluating Model Testing and Model Checking for Finding Requirements Violations in Simulink Models](https://mlsanity.com/paper/1905.03490/) (2019)
- [Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering](https://mlsanity.com/paper/2307.04599/) (2023)
- [Replicability Study: Corpora For Understanding Simulink Models & Projects](https://mlsanity.com/paper/2308.01978/) (2023)
- [Towards Counterfactual Explanation and Assertion Inference for CPS Debugging](https://mlsanity.com/paper/2604.07679/) (2026)

## Abstract (arXiv)

Effective AI adoption in cyber-physical systems (CPS) depends on embedding design knowledge into engineering practice. Yet as AI-enabled components increasingly replace analytically derived control laws, this occurs without a systematic understanding of how controller architectures differ or remain similar across paradigms. We address this gap with an empirical study of traditional and AI-enabled Simulink controllers, guided by a literature-derived taxonomy of ten structural categories and nine functional roles. The study analyzes 62 real-world models spanning 8 controller types and 10 application domains, and surveys 13 practitioners, identifying three architectural tensions. First, subsystem organization dominates all controller structures regardless of paradigm, occupying 68-72% of controller footprint, while core control logic occupies minimal space. Second, AI-enabled controllers rely heavily on discrete dynamics and user-defined abstraction, categories largely absent from AI literature, exposing a gap between described and implemented architectures. Third, constraint enforcement blocks largely disappear from AI-enabled models despite practitioner expectations. This reveals a misalignment where safety mechanisms shift from explicit structure to implicit training-time artifacts, breaking traceability.