用大模型自动生成化工控制策略，一键完成设计与调优。
An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models
- 大模型分步生成控制代码，每步都验证通过才继续。
- 自动调优使系统性能提升26.5%，主要改善压力响应速度。
- 适合工业控制自动化研究者，尤其关注智能流程设计的人。
我们提出一种基于大型语言模型的结构化工作流，实现从动态过程模型中自动设计多变量控制策略。该工作流将设计任务分解为受约束的代码生成步骤：过程接口构建、归一化处理、操纵变量-被控变量配对、控制器定义、闭环仿真、场景生成、性能评估及基于贝叶斯优化（BO）的调参。生成的代码在进入下游任务前执行并验证，失败则利用反馈修复。该方法在非线性燃气预热器基准测试中验证，该系统存在耦合的压力与温度动态。生成的工作流输出一个物理解释性强的解耦式比例积分（PI）反馈-前馈控制结构及可执行调优环境。贝叶斯优化使闭环性能目标（综合设定值跟踪与扰动抑制误差）相对初始控制器降低约26.5%，主要得益于压力回路瞬态性能提升。该结果量化了自动调优阶段的效果，而非与人工设计控制器对比。结果表明，基于结构化大模型代码生成可构建可执行的控制设计工作流，但需在更大范围的全流程控制基准上进一步验证。
原文摘要 · Abstract (English)
We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into constrained code-generation steps: plant-interface construction, normalization, manipulated-variable controlled-variable (MV-CV) pairing, controller specification, closed loop simulation, scenario generation, performance evaluation and Bayesian-optimization (BO) based tuning. Generated artifacts are executed and validated before downstream tasks proceed, and failed artifacts are repaired using validation feedback. The proposed approach is demonstrated on a nonlinear gas-preheater benchmark with coupled pressure and temperature dynamics. The generated workflow produces a physically consistent decentralized PI (proportional-integral) feedback-feedforward control structure and an executable tuning environment. Bayesian optimization reduces the closed loop performance objective, which aggregates set-point tracking and disturbance-rejection errors for the controlled variables, by approximately 26.5% relative to the initial controller generated by the workflow, mainly through improved pressure-loop transient performance. This figure quantifies the automated tuning stage rather than a comparison against a manually designed controller. The results demonstrate the feasibility of using structured large-language-model-based code generation to construct executable control-design workflows, while also highlighting the need for broader validation on larger plantwide-control benchmarks.
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