eess.SY · 2026-07-30 · No. 69
Systems and Control, 2026-07-30.
1 new papers in eess.SY. Titles, authors,
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01 — The papers
1 entries-
01
A Physics-Informed Framework for PID Tuning of Chemical Processes Using Large Language Model Agents
Zhoupeng Shou, Xiaodong Hong, Congjing Ren, Jingdai Wang, Yongrong Yang, Zuwei Liao
eess.SY · cs.AI
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,...
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