用多智能体大模型解决控制工程问题，用户说人话就能得答案。
LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineer
- 分角色协作：主控+专用辅助智能体分工处理建模、分析、仿真等任务。
- 83%任务成功解决，单个智能体平均成功率87%，越强模型表现越好。
- 无需控制理论基础，自然语言提问即可获得实时完整解法，适合工程新手。
本研究提出 LLM-Agent-Controller，一种用于解决控制工程（控制理论）中各类问题的多智能体大语言模型系统。该系统包含一个中央控制器与多个专业辅助智能体，分别负责控制器设计、模型表示、控制分析、时域响应和仿真等任务。一名监督者负责高层决策与流程协调，提升系统可靠性与效率。系统集成检索增强生成（RAG）、思维链推理、自我批评与修正、高效记忆管理及自然语言交互能力，使用户无需具备控制理论知识，仅需用自然语言描述问题，即可获得实时完整解答。为评估系统性能，我们提出新的评价指标，涵盖个体智能体与整体系统，并在三类先进大模型上测试五类控制理论问题。定性对话分析覆盖所有核心服务。结果表明，该系统成功解决83%的一般任务，各智能体平均成功率达87%。模型越先进，性能越优。研究证明多智能体大模型架构在解决复杂领域问题上的潜力。通过专业化分工、监督控制与先进推理，该系统提供可扩展、鲁棒且易用的解决方案框架，适用于多种技术领域。
原文摘要 · Abstract (English)
This study presents the LLM-Agent-Controller, a multi-agent large language model (LLM) system developed to address a wide range of problems in control engineering (Control Theory). The system integrates a central controller agent with multiple specialized auxiliary agents, responsible for tasks such as controller design, model representation, control analysis, time-domain response, and simulation. A supervisor oversees high-level decision-making and workflow coordination, enhancing the system's reliability and efficiency. The LLM-Agent-Controller incorporates advanced capabilities, including Retrieval-Augmented Generation (RAG), Chain-of-Thought reasoning, self-criticism and correction, efficient memory handling, and user-friendly natural language communication. It is designed to function without requiring users to have prior knowledge of Control Theory, enabling them to input problems in plain language and receive complete, real-time solutions. To evaluate the system, we propose new performance metrics assessing both individual agents and the system as a whole. We test five categories of Control Theory problems and benchmark performance across three advanced LLMs. Additionally, we conduct a comprehensive qualitative conversational analysis covering all key services. Results show that the LLM-Agent-Controller successfully solved 83% of general tasks, with individual agents achieving an average success rate of 87%. Performance improved with more advanced LLMs. This research demonstrates the potential of multi-agent LLM architectures to solve complex, domain-specific problems. By integrating specialized agents, supervisory control, and advanced reasoning, the LLM-Agent-Controller offers a scalable, robust, and accessible solution framework that can be extended to various technical domains.
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