发表机构
Imperial College London; University College London（帝国理工学院; 伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出由大语言模型驱动的工作流程，用于从动态过程模型自动生成和调整控制策略，将设计任务分解为多步骤，经执行验证和修复，在气体预热器基准测试中生成控制结构与环境，贝叶斯优化降低闭环性能目标约26.5%，证明方法可行性。
AI 中文摘要
我们提出了一种结构化的、由大语言模型驱动的工作流程，用于从动态过程模型进行自动多变量控制设计。该工作流程将设计任务分解为受限的代码生成步骤，包括工厂接口构建、归一化、操纵变量-控制变量配对、控制器规格设定、闭环仿真、场景生成、性能评估以及基于贝叶斯优化的调整。生成的工件在下游任务进行之前执行并验证，失败的工件利用验证反馈进行修复。我们在具有压力和温度动态耦合的非线性气体预热器基准测试中展示了所提出的方法。生成的工作流程产生了一个物理上一致的分散式比例积分反馈-前馈控制结构和一个可执行的调整环境。贝叶斯优化将闭环性能目标（该目标汇总了控制变量的设定点跟踪和干扰抑制误差）相对于工作流程生成的初始控制器降低了约26.5%，主要是通过改善压力回路的瞬态性能。该结果量化了自动调整阶段，而非与手动设计的控制器进行比较。结果证明了使用基于结构化大语言模型的代码生成来构建可执行控制设计工作流程的可行性，同时也强调了在更大的全厂控制基准上进行更广泛验证的必要性。
英文摘要
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.
