这篇论文用LLM自动调PID参数，小模型Qwen3-0.6B调参成功率94%，比大模型还稳。
该框架利用大语言模型（如DeepSeek-V4-Flash、Qwen3.7-Plus）模拟工程师工作流，通过闭环响应特征生成并迭代修正PID增益。在100个FOPDT和100个SOPDT测试用例上，托管LLM最终成功率达75-89%和77-79%。对于本地部署的Qwen3-0.6B，监督微调使首次推荐成功率为86.5%，结合物理信息GRPO进一步提高至94.0%，主要改善了首次尝试可靠性和稳定性裕度。
A Physics-Informed Framework for PID Tuning of Chemical Processes Using Large Language Model Agents
PID tuning for chemical processes commonly relies on identified process models, whereas plant engineers often retune loops iteratively by observing responses, diagnosing deficiencies, adjusting gains, and validating the result. This work formalizes this engineer-like workflow in a language-model-assisted PID tuning framework applicable to both large and small language models (LLMs/SLMs). Hosted LLMs receive closed-loop response features, control-engineering diagnoses, tuning preferences, and internal model control (IMC)-based demonstrations to generate and iteratively correct PID gains under common acceptance criteria. For local deployment, Qwen3-0.6B is adapted through supervised fine-tuning (SFT) with simulation-verified IMC targets and physics-informed group relative policy optimization (PI-GRPO) with non-compensable stability and performance rewards. On 100 first-order plus dead time (FOPDT) and 100 second-order plus dead time (SOPDT) test cases, hosted LLMs (DeepSeek-V4-Flash and Qwen3.7-Plus) achieve final success rates of 75-89% and 77-79%, respectively. As for Qwen3-0.6B, supervised fine-tuning raises first-recommendation success to 86.5%, and PI-GRPO further increases it to 94.0%, primarily improving first-attempt reliability and stability margins.