Explore/agent app/AI Control Scientist: LLM-driven Agentic System for Automated Control Design
A

Haiteng Wang, Weihao Li, Jing Zhang, Lei Ren/AI Control Scientist: LLM-driven Agentic System for Automated Control DesignUnknown

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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AgentHub Score
55 / 100
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55Score
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40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
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58C
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2026-08-292026-08-31
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✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

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AgentHub Score

55
Score 55/100
Below average

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