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This empirical study reveals a critical architectural shift in cyber-physical systems where safety mechanisms transition from explicit code structures to implicit artifacts within AI training processes, potentially compromising traceability. The research highlights that subsystem organization dominates controller footprints regardless of paradigm, while AI-enabled models rely heavily on discrete dynamics and user-defined abstractions absent from current literature. For engineers integrating AI into control loops, these findings underscore the urgent need to reconcile implementation realities with theoretical descriptions to maintain rigorous safety standards.
An Empirical Study of Architectural Shift from Traditional to AI-Enabled Simulink Controllers
arXiv:2609.38504v1 Announce Type: cross Abstract: 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.
Abstract: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.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
D.2.11; D.2.2; C.3; I.2.6
arXiv:2609.38504 [cs.SE]
(or arXiv:2609.38504v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2609.38504 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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