mlsanityData to 14 Oct 2026

ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise

arXiv 2410.19811 ↗First posted 17 Oct 2024eess.SY cs.AI cs.CLcited by 21

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors including aerospace, automotive systems, power grids, and robotics. Despite advances made by Large Language Models (LLMs) in various domains, their application in control system design remains limited due to the complexity and specificity of control theory. To bridge this gap, we introduce ControlAgent, a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise. ControlAgent encodes expert control knowledge and emulates human iterative design processes by gradually tuning controller parameters to meet user-specified requirements for stability, performance, and robustness. ControlAgent integrates multiple collaborative LLM agents, including a central agent responsible for task distribution and task-specific agents dedicated to detailed controller design for various types of systems and requirements. ControlAgent also employs a Python computation agent that performs complex calculations and controller evaluations based on standard design information provided by task-specified LLM agents. Combined with a history and feedback module, the task-specific LLM agents iteratively refine controller parameters based on real-time feedback from prior designs. Overall, ControlAgent mimics the design processes used by (human) practicing engineers, but removes all the human efforts and can be run in a fully automated way to give end-to-end solutions for control system design with user-specified requirements. To validate ControlAgent's effectiveness, we develop ControlEval, an evaluation dataset that comprises 500 control tasks with various specific design goals. The effectiveness of ControlAgent is demonstrated via extensive comparative evaluations between LLM-based and traditional human-involved toolbox-based baselines.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 39 of its 88 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

39 of 88 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System23 Feb
  2. 2023A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration3 Oct
  3. 2023A Survey on Large Language Model based Autonomous Agents22 Aug
  4. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate14 Aug
  5. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug
  6. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents5 Jun
  7. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar

Other references · 32

  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  3. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  4. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  5. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  6. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
  7. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
  8. 2023Reasoning with Language Model is Planning with World Modelcited by 861
  9. 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 631
  10. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  11. 2023ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Casescited by 393
  12. 2024CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challengescited by 282
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  2. 2022TALM: Tool Augmented Language Modelscited by 197
  3. 2023OpenAGI: When LLM Meets Domain Expertscited by 197
  4. 2023Language Models Meet World Models: Embodied Experiences Enhance Language Modelscited by 124
  5. 2023Mindstorms in Natural Language-Based Societies of Mindcited by 107
  6. 2023A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writingcited by 99
  7. 2024AnalogCoder: Analog Circuit Design via Training-Free Code Generationcited by 84
  8. 2021MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extractioncited by 83
  9. 2024Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultracited by 37
  10. 2024Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sectorcited by 30
  11. 2024AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Portingcited by 29
  12. 2023Structured Chemistry Reasoning with Large Language Modelscited by 28
  13. 2024Foundation Models for the Electric Power Gridcited by 28
  14. 2023Flows: Building Blocks of Reasoning and Collaborating AIcited by 25
  15. 2024Benchmarking the Capabilities of Large Language Models in Transportation System Engineering: Accuracy, Consistency, and Reasoning Behaviorscited by 13
  16. 2024INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformerscited by 8
  17. 2024Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Dalinecited by 7
  18. 2024SPICED: Syntactical Bug and Trojan Pattern Identification in A/MS Circuits using LLM-Enhanced Detectioncited by 7
  19. 2024Constructing Mechanical Design Agent Based on Large Language Modelscited by 5
  20. 2024Unlocking the Potential: Benchmarking Large Language Models in Water Engineering and Researchcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 8; 2026: 12.

21 papers.

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  2. 2024Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Frameworkcited by 19
  3. 2026Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimizationcited by 16
  4. 2025LLM-guided Chemical Process Optimization with a Multi-Agent Approachcited by 12
  5. 2025LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineercited by 5
  6. 2025LogicGuard: Improving Embodied LLM agents through Temporal Logic based Criticscited by 5
  7. 2025Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditionscited by 4
  8. 2026Self-Evolving Scientific Agent Designs Physically Reasoned White-Box Fluid Controlcited by 4
  9. 2025Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challengescited by 3
  10. 2025Dual Filter: A Transformer-like Inference Architecture for Hidden Markov Modelscited by 0
  11. 2025LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Modelscited by 0
  12. 2026Meituan Merchant Business Diagnosis via Policy-Guided Dual-Process User Simulationcited by 0
9 more
  1. 2026Numbers Beat Words: A Rigorous On-Premise Benchmark for Coupled MIMO Controller Tuningcited by 0
  2. 2026Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correctioncited by 0
  3. 2026Safety-Gated Agentic Supervisory Control on a Coupled Distillation Benchmark: Regime Map, Auditable Gate, and Co-Design Findingscited by 0
  4. 2026Benchmarking and Reasoning Distillation of Large Language Models for Feedback Controller Design in Complex Dynamical Systemscited by 0
  5. 2026AI Control Scientist: LLM-driven Agentic System for Automated Control Designcited by 0
  6. 2026Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systemscited by 0
  7. 2026little m: An AI Agent for Industrial Process Optimizationcited by 0
  8. 2026Requirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release Authoritycited by 0
  9. 2026SimuVerity: Benchmarking Agents for Engineering-Grade Simulink Model Generationcited by 0

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