arXiv:2607. 21292v1 Announce Type: new Abstract: We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models.
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AI Control Scientist (AICS) is a large language model–driven agent that automatically generates optimized controllers from language design requirements. It comprises a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show AICS outperforms existing automated baselines in design success rate and optimization efficiency, enabling the creation of multiple representative control systems.
arXiv:2607. 26594v1 Announce Type: cross Abstract: 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.
arXiv:2608. 11220v1 Announce Type: new Abstract: Nowadays, the creation of a process flow diagram (PFD) and its subsequent transformation into a piping and instrumentation diagram (P&ID) is predominantly performed manually.
The paper introduces AI Control Scientist (AICS), a large language model–driven agent that automatically generates optimized controllers from language design requirements. AICS consists of a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show that AICS outperforms existing automated baselines in design success rate and optimization efficiency, demonstrating its potential to shift control system design from human‑driven to agent‑driven approaches.
The paper introduces little m, an AI agent that helps formulate industrial process control models by combining a domain-specific knowledge repository with LLM-driven interaction. It tackles the challenge of converting messy real-world specifications, including natural language and spatial diagrams, into rigorous mathematical optimization models. The authors also present IPC-Bench, a multimodal dataset of 50 canonical scenarios, and show through automated and human evaluations that little m outperforms state‑of‑the‑art LLMs in generating semantically correct models.
arXiv:2607. 09713v1 Announce Type: new Abstract: A key step toward autonomous industrial operation is the ability to create and reconfigure control policies from natural-language requirement specifications, with minimal or no manual redesign.