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AI Control Scientist: LLM-driven Agentic System for Automated Control Design

Haiteng Wang, Weihao Li, Jing Zhang, Lei Ren

arXiv:2608.26780v1cs.AI

TL;DR

Control design requires substantial expert knowledge and manual tuning, while natural-language requirements leave control objectives and constraints mathematically implicit. AICS addresses this with a role-specialized LLM agentic workflow that models requirements, generates controllers, and tunes parameters; experiments show higher design success and optimization efficiency than automated baselines, with expert-comparable performance.

  • Problem

    Control design is important in modern industry, but workflows rely heavily on expert knowledge and manual tuning, while natural-language requirements do not provide explicit mathematical formulations of objectives and constraints.

  • Method

    A role-specialized LLM agentic system uses Task Modeling, Controller Design, and Parameter Tuning Agents for requirement formalization, controller generation, and closed-loop parameter optimization.

  • Results

    The system outperforms existing automated baselines in design success rate and optimization efficiency across representative control systems, achieving performance comparable to domain experts.

  • Takeaways & Limitations

    The results support a scalable and generalizable paradigm for AI-native control engineering.

Abstract

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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.

I. INTRODUCTION

Control design is important across modern engineering, but increasingly complex systems expose the limits of manual workflows and direct LLM code generation. AICS addresses these challenges through specialized agents, logic-guided synthesis, and solver-assisted tuning.

  • Motivation: Increasing system complexity makes heuristic control tuning labor-intensive, time-consuming, and difficult to scale under couplings, uncertainties, and multiple constraints.Expert knowledge is also fragmented and difficult to convert into reusable automated workflows.
  • Challenges: General-purpose LLMs often lack the grounded physical reasoning needed to synthesize controllers handling stability margins, saturation, coupling, and dynamic trade-offs.The challenge spans multiloop PID, constrained MPC, and nonlinear feedback compensation.
  • Challenges: Ambiguous natural-language requirements do not directly specify the mathematical objectives and constraints required for industrial control design.Existing LLMs struggle to map descriptions such as avoiding overshoot or improving disturbance rejection into rigorous formulations.
  • Challenges: Unreliable numerical reasoning makes direct LLM parameter optimization unsuitable for fine-grained tuning, leaving practitioners dependent on repeated simulation-based expert adjustment.The bottleneck includes PID gains, observer poles, and MPC weighting matrices.
  • Proposed system: AICS uses a Task Modeling Agent, Controller Design Agent, and Parameter Tuning Agent to translate requirements into models, generate controller code, and refine parameters under closed-loop criteria.The agents collaborate iteratively to emulate the workflow of experienced control engineers.
  • Results: Experiments show higher design success and optimization efficiency than automated baselines, with performance comparable to domain experts across representative control systems.The paper positions the workflow as a scalable and generalizable paradigm for AI-native control engineering.
  • Proposed system: The system combines logic-guided controller generation with planner-solver optimization, using inferred system characteristics and external numerical solvers for precise tuning under practical constraints.The Design Blueprint guides code synthesis, while the LLM supplies search bounds and priors to numerical solvers.

II. RELATED WORK

Related work advances LLM-based code generation through iterative feedback, search, execution semantics, and multi-agent design. In control engineering, these ideas are being adapted to translate language into executable controllers and refine them across increasingly complex scenarios.

  • LLMs for code generation: LLM software-engineering systems have progressed from code completion toward autonomous program synthesis and iterative refinement.Closed-loop reflection and planning methods treat code generation as a nonlinear state-space search.
  • LLMs for code generation: Execution feedback and reinforcement-learning formulations improve functional code generation by aligning textual outputs with variable trajectories, compiler observations, and interactive environments.CodeRL+ and InterCode exemplify this execution-centered direction.
  • LLMs for control engineering: Control-oriented code agents transform natural-language instructions into executable Python or C++ control programs, with later systems adding environmental feedback and persistent skill libraries.These approaches bridge high-level requirements and implementable control behavior.
  • LLMs for control engineering: ControlAgent and AgenticControl use specialized multi-agent workflows and computation tools to evaluate frequency-domain behavior and refine controller parameters across increasingly complex scenarios.AgenticControl extends the architecture to six agents and addresses nonlinear dynamics and parametric uncertainty.

III. METHOD

The method decomposes constrained, non-convex control design into semantic formalization, verified code generation, and solver-guided parameter tuning. Three agents jointly convert natural-language objectives into feasible controllers through closed-loop simulation and feedback.

  • Framework: Control design is formulated as a high-dimensional, constrained, non-convex optimization problem over a control law and its parameters.The objective incorporates system evolution and physical or performance constraints.
  • Framework: The pipeline decomposes global optimization into Semantic Formalization, Code Generation, and Parameter Tuning stages executed by three agents.This sequential workflow starts from natural-language instructions and ends with tuned controller parameters.
  • Semantic Formalization: The Task Modeling Agent removes semantic ambiguity and converts user objectives into deterministic system parameters, mathematical definitions, and performance constraints.It uses a translation knowledge base and control-standard verification and analysis tools.
  • Code Generation: The Controller Design Agent selects a control strategy and generates code through inference and synthesis, then iteratively refactors errors identified by syntax and control-theoretic verification.Critical system features such as time delays and coupling inform a Design Rationale.
  • Parameter Tuning: The Parameter Tuning Agent constructs a simulator-based closed loop and uses LLM reasoning to define rewards and search ranges for efficient numerical optimization.The tuned parameters are produced by searching within the resulting parameter space rather than by direct LLM arithmetic.
  • Framework: The complete process targets end-to-end automated control design while preserving theoretical correctness and engineering feasibility.The framework integrates natural-language input, controller synthesis, verification, and tuning.

B. Task Modeling Agent (TMA)

The Task Modeling Agent converts ambiguous natural-language requirements into a structured control specification, then formalizes and verifies its models and constraints.

  • The TMA maps unstructured input into a specification containing the plant dynamic model M and control constraints Cconstraint.
  • Semantic formalization translates engineering descriptions into a deterministic system model and numerical performance boundaries.
  • The constraint set separates physical execution boundaries Cphys from dynamic performance metrics Cperf.
  • After formalization, verification converts LTI systems to standard matrix form and checks stability, physical feasibility, and constraint consistency.

C. Controller Design Agent (CDA)

The Controller Design Agent generates controller code from plant characteristics and formalized specifications, then uses theory-aware verification and feedback to correct invalid implementations.

  • The CDA uses a system-characteristic-driven paradigm to tailor controller logic to the plant rather than relying on generic text-to-code generation.
  • It infers dynamical descriptors from Sspec and uses control knowledge to determine the control law π and a theoretical blueprint Blogic.
  • Conditioning code generation on Blogic makes the implementation a deterministic consequence of identified system characteristics rather than a stochastic guess.
  • Verification combines syntax checking with control-theory checks, including controllability criteria, to intercept invalid logic.
  • When verification fails, exception and theory-violation information is fed back to the LLM for controller regeneration until verification succeeds.

D. Parameter Tuning Agent (PTA)

The Parameter Tuning Agent separates LLM-based optimization reasoning from numerical search, iteratively refining search configurations and parameters using simulation feedback.

  • The PTA addresses numerical hallucination by decoupling symbolic reasoning from numerical computation in a meta-optimization paradigm.
  • Each meta-optimization configuration Φk contains a parameter search domain Ωk and a search reward function Rk(·).
  • Using trial history, the LLM adjusts the reward function or search space instead of directly guessing control parameters.
  • Under Φk, a numerical optimizer builds a surrogate objective model and samples Ωk by maximizing an acquisition function α(θ).
  • Within a limited simulation budget, the optimizer obtains a parameter vector θ∗ that maximizes expected reward.
  • After each closed-loop simulation, the system calculates performance metrics and updates historical memory for subsequent refinement.
  • Algorithm 1 specifies the complete double-layer iterative tuning process driven by simulation.

IV. EXPERIMENTS

The experiments evaluate the proposed agentic control-design system through a series of automated tuning and simulation procedures.

  • The study evaluates AICS through a series of comprehensive experiments.
  • Algorithm 1 takes a controller, target specifications, a maximum refinement-round count K, and a maximum simulation count N per round.
  • Each refinement round uses the LLM to design or refine an optimization configuration before numerical optimization proceeds.
  • The procedure compiles and simulates proposed parameters, debugging controller code when execution fails.
  • Successful simulations are evaluated, scored, and added to optimizer and refinement histories while retaining the best parameters.
  • The procedure returns when the target specification is met, otherwise it builds feedback for the next refinement round.

A. Experimental Setup

The evaluation spans diverse dynamic systems and measures both controller success and optimization efficiency. Comparisons include baseline methods and visual analyses of tuning convergence.

  • The benchmark covers stable, unstable, time-delay, and higher-order complex systems, with 50 independent test cases per category.
  • Pass Rate measures whether controllers satisfy control objectives, performance metrics, and constraints.
  • Number of Iterations measures the average refinement steps needed to reach the optimal parameter configuration.
  • Figure 3 tracks time-response evolution during tuning, while Figure 4 summarizes iteration-count distributions for successful trials.

B. Main Results and Baseline Comparison

Across increasingly difficult control systems, AICS outperforms automated baselines in success rate and optimization efficiency. The strongest gains appear in nonlinear unstable and higher-order systems, where iterative feedback and numerical optimization address baseline weaknesses.

  • As complexity increases, vanilla Zero-shot and Few-shot methods fail completely in second-order unstable environments, with a 0% pass rate.
  • On highly nonlinear second-order unstable systems, AICS achieves a 90% pass rate using 2.44 iterations, versus ControlAgent’s 74% pass rate and PIDtune’s 12%.
  • On higher-order complex systems, AICS reaches a 74% pass rate, exceeding ControlAgent at 54%, Few-shot with feedback at 40%, and PIDtune at 50%.
  • ControlAgent reaches around 6 median iterations on challenging second-order unstable systems, with an upper quartile of 7 and outliers up to 9.
  • AICS more accurately uses historical feedback to identify performance flaws and rapidly execute corrective parameter updates than ControlAgent.
  • The ablation results indicate that removing numerical optimization causes severe pass-rate degradation, especially for higher-order and second-order unstable systems.

C. Ablation Study on Multi-Agent Mechanisms

Ablations show that numerical optimization and historical-feedback guidance are both important: the former supports reliable computation, while the latter accelerates and improves parameter search. Larger language-model backbones are especially beneficial for complex control tasks.

  • Ablation Design: The ablations remove the Numerical Optimization and LLM Historical Feedback Guidance modules to test the necessity of these mechanisms.
  • Numerical Optimization: Removing numerical optimization causes severe pass-rate collapse on unstable and higher-order systems, with slower convergence and a lower final performance bound.The result supports decoupling symbolic reasoning from numerical computation in complex control design.
  • Meta-Optimization Guidance: Removing meta-optimization guidance slows early convergence and limits the final asymptotic pass rate to approximately 84%, versus over 90% for the full system.The guidance dynamically narrows the parameter search space using trial history.
  • Model Scale and Reasoning: Smaller models perform comparably on delay and first-order systems, while stronger reasoning and code synthesis improve performance on second-order unstable and higher-order systems.
  • Model Scale and Reasoning: Qwen3-235B reaches a 74% pass rate on higher-order tasks, compared with 54% for the 8B model.

E. Case Study: End-to-End Autonomous Design

The case study presents an end-to-end autonomous workflow that formalizes requirements, generates and repairs controller code, tunes parameters through simulation, and verifies the resulting design against industrial criteria.

  • Workflow: The execution proceeds through Semantic Formalization, Code Generation with error self-correction, and Simulation-driven Parameter Tuning.
  • Semantic Formalization: The Task Modeling Agent maps linguistic constraints into deterministic parameters and establishes a mathematical feasible region through open-loop analysis.Example parameters include max_overshoot = 15.1 and max_settling_time = 5.3.
  • Code Generation: The Controller Design Agent captures execution errors and refactors faulty controller logic using a control-theory verification engine without human intervention.
  • Parameter Tuning: The Parameter Tuning Agent delegates numerical search to an optimizer while the LLM configures the reward function and search boundaries, producing Q = diag([50]) and R = diag().
  • Output Verification: The verified controller achieves 4.8% overshoot, 0.1 s settling time, and 8 × 10^-6 steady-state error, all below the specified limits.
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