LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineer

2025-05-26

AI Summary

Solving control engineering problems traditionally requires deep domain expertise, posing a significant barrier to accessibility and automation. Method: This paper proposes LLM-Agent-Controller—the first general-purpose, multi-agent large language model system tailored for control theory. It employs a domain-specific multi-agent architecture integrating retrieval-augmented generation (RAG), chain-of-thought reasoning, self-critique refinement, efficient memory management, and role-based collaboration. Users submit queries in natural language; the system autonomously executes end-to-end tasks including dynamic modeling, controller synthesis, stability analysis, time-domain response evaluation, and simulation. Contribution/Results: Innovations include supervised workflow orchestration and zero-barrier interaction, enabling real-time problem solving without prior control theory knowledge. Evaluated on five canonical control problem classes, the system achieves an overall success rate of 83% and an average per-agent success rate of 87%. Performance scales significantly with base model capability, demonstrating robust generalization and practical applicability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Distributed Problem Solving
• Intelligent Robots: Behavior Learning & Control

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Develops a multi-agent LLM system for control engineering tasks
• Enables plain language input without Control Theory knowledge
• Integrates specialized agents for scalable, domain-specific solutions

Innovation

• Multi-agent LLM system for control engineering
• Integrates RAG and Chain-of-Thought reasoning
• Supervisor enhances reliability and efficiency