用五阶段流程让物理系统设计可追溯,实现人机协同设计
Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case
- 以规范为契约,分五阶段推进系统设计,保持设计过程可追踪
- 在两个不同平台的倒立摆控制设计中验证流程兼容性
- 适合需透明化、可复现的硬件与控制系统研发团队
工程系统设计(如机电、控制或嵌入式系统)常缺乏系统性,需求隐含且意图到参数的可追溯性几乎缺失。现有规范驱动方法多针对软件,而人工智能辅助工具通常只介入解决方案生成阶段,而非问题定义。人机协作在物理系统设计中仍待深入探索。本文提出 Design-OS,一个轻量级、规范驱动的设计工作流,包含五个阶段:概念定义、文献调研、概念设计、需求定义和设计定义。规范作为人与AI代理之间的共享契约,各阶段产出结构化成果,维持可追溯性并支持代理增强执行。将 Design-OS 与需求驱动设计、系统化设计框架及AI辅助设计流程对比,并在两个旋转倒立摆平台(开源 SimpleFOC 反应轮与商业 Quanser Furuta 摆)上进行控制设计案例验证,展示同一规范驱动流程可适应本质不同的实现方式。完整模板与设计案例成果已公开于公共仓库,支持可复现与复用。该流程使设计过程透明可审计,将规范驱动的AI编排从软件扩展至物理系统设计。
原文摘要 · Abstract (English)
Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.
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