用大模型自动设计控制器，告别人工调参
AI Control Scientist: LLM-driven Agentic System for Automated Control Design
- 三阶段智能体解析需求、生成结构、优化参数
- 在多种控制任务中成功率与效率超越现有自动化方法
- 适合工业控制研发人员快速构建先进控制系统
控制系统设计对现代工业至关重要，如化工过程温度调控和航空发动机控制。然而，传统设计流程高度依赖专家知识和大量手动参数调整，效率与可扩展性受限。为此，本文提出首个基于大语言模型（LLM）的智能体系统AI Control Scientist（AICS），可从自然语言要求自动生成优化控制器。具体包括：任务建模智能体将用户需求转化为工程约束；控制器设计智能体生成候选控制器结构与可执行代码；参数调优智能体在闭环性能标准下优化控制器参数。实验表明，该智能体系统能自动生成多种典型控制系统，在设计成功率和优化效率上均优于现有自动化基线。本工作有望推动控制系统设计从人驱动转向智能体驱动，为模型预测控制等先进系统设计铺平道路。
原文摘要 · Abstract (English)
Control system design is critical for modern industry, such as chemical process temperature regulation and aero-engine control. However,traditional control design workflows rely heavily on expert knowledge and extensive manual parameter tuning, resulting in limited efficiency and scalability. To this end, this paper proposes AI Control Scientist (AICS), the first large language model (LLM)-driven agent capable of automatically generating optimized controller from language design requirements. Specifically, a Task Modeling Agent interprets user requirements to engineering constraints; a Controller Design Agent generate candidate controller structures and executable code; and a Parameter Tuning Agent refine controller parameters under closed-loop performance criteria. Experiments demonstrate that the proposed agentic system can automatically generate multiple representative control systems, outperforms existing automated baselines in both design success rate and optimization efficiency. This work has the potential to transform control system design from human-driven to agent-driven, paving the way for model predictive control and other advanced control systems design.
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