发表机构
University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过分析62个真实Simulink模型并调查13位从业者，实证揭示了AI赋能控制器在架构上依赖离散动态与用户抽象、弱化显式约束执行，导致安全机制可追溯性断裂的问题。
AI 中文摘要
在信息物理系统（CPS）中有效采用人工智能依赖于将设计知识嵌入工程实践。然而，随着AI赋能组件日益取代解析推导的控制律，这种转变并未伴随对控制器架构在不同范式间差异或相似性的系统性理解。我们通过一项针对传统与AI赋能Simulink控制器的实证研究来填补这一空白，该研究以文献衍生的分类法为指导，涵盖十个结构类别和九个功能角色。研究分析了跨越8种控制器类型和10个应用领域的62个真实世界模型，并调查了13位从业者，识别出三种架构张力。首先，子系统组织在所有控制器结构中占据主导地位，无论范式如何，占控制器足迹的68-72%，而核心控制逻辑仅占极小空间。其次，AI赋能控制器高度依赖离散动态和用户自定义抽象，这些类别在AI文献中基本缺失，暴露出描述架构与实现架构之间的差距。第三，约束执行模块在AI赋能模型中大量消失，尽管从业者对此有所期望。这揭示了安全机制从显式结构转向隐式训练时产物的错位，破坏了可追溯性。
英文摘要
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.
CommentsAccepted at the 33rd Asia-Pacific Software Engineering Conference (APSEC 2026)
