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

Authors: Xingang Guo, Darioush Keivan, Usman Ahmed Syed, Lianhui Qin, Huan Zhang, Geir Dullerud, Peter Seiler, Bin Hu
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Automated Control System Design, LLM Agent

Abstract:
Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors, including aerospace, automotive systems, industrial processes, 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 (e.g. settling time), and robustness (e.g., phase margin). Specifically, 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. In addition to LLM agents, ControlAgent employs a Python computation agent that performs complex control gain calculations and controller evaluations based on standard design information (e.g. crossover frequency, etc) 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. Comparative evaluations between LLM-based and traditional human-involved toolbox-based baselines demonstrate that ControlAgent can effectively carry out control design tasks, marking a significant step towards fully automated control engineering solutions.

Decision: reject
Reviewer scores: 6, 5, 6

Reviews and discussion:
<thread>
creation date:
1729281383907

thread_type:
official_review

<thread_object>
creator: reviewer_w6fy

<content>
summary:
This paper describes a composite LLM-based system for control tasks which attempts to design controllers, represented as Python code, for control problems with specific requirements, namely stability, phase margin, and settling time. 

While this paper is decently presented and seems to achieve decent results, I am uncertain about recommending it for ICLR. Primarily, the paper seems highly domain-specific and engineering-focused, rather than more general cutting-edge academic research. Still, it is a good engineering system. Secondly, I am uncertain about the evaluation. 

The proposed method is essentially a domain-specific application of LLM-modulo, e.g. an interative prompt with a verifier and critiques [1].

[1] Kambhampati, S., Valmeekam, K., Guan, L., Verma, M., Stechly, K., Bhambri, S., ... & Murthy, A. B. Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks. In Forty-first International Conference on Machine Learning.

strengths:
This paper addresses the issue of designing controllers using LLMs, in particular with specific stability, phase margin, and settling times. 

The overall system runs in a loop where a the designed controller is run and the system provides feedback based on a history of designs and how well they performed.

weaknesses:
It seems guarantees would be desirable when working with control systems, and I assume the problem requirements are meant to be guarantees. However, I feel the paper would be made a lot stronger by discussing guarantees at length. 

The evaluation methods seem like they could be improved, in particular I would like the authors to clarify about "a system is considered successfully designed if at least one of the multiple independent trials results in a successful design". It seems this would greatly skew the statistics, since failures are being filtered out. I also don't see reporting of how many samples are taken to achieve the reported success rates. 

Given the unpredictable and error-prone nature of LLMs, I am skeptical that the overall system can work without a human in the loop or method for filtering correct answers. Also, it seems like intermediate mistakes in generation (e.g. a hallucinated constant) would collapse the entire system, so I would expect it to be rather fragile. To the extent that the proposed method works, I am curious what the authors attribute it to?

While the method is interesting, it seems to be an incomplete solution to a highly domain-specific problem, so I'm unsure about the larger impact of the work, e.g. the paper doesn't give much insight into designing general LLM-based systems.

questions:
How much sampling is done of LLM-generated designs? e.g. is the budget 10 designs?
</content>
</thread_object>

</thread>
<thread>
creation date:
1729442832374

thread_type:
official_review

<thread_object>
creator: reviewer_tosd

<content>
summary:
This paper introduces a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise. However, the writing style is confusing, making it hard to follow their ideas. I suggest the authors improve their academic writing skills by making the abstract more precise and brief, adding the approach section, and reorganizing the corresponding method section. Moreover, I do not know what scenarios the authors implemented or simulated for the experiments. There is no background information or introduction. Generally, this paper needs to improve largely.

strengths:
This paper proposes a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise to bridge the the complexity and specificity in control system design.

weaknesses:
The paper's writing style is confusing, making it hard to follow their ideas. I suggest the authors improve their academic writing skills by making the abstract more precise and brief, adding the approach section, and reorganizing the corresponding method section. Moreover, I do not know what scenarios the authors implemented or simulated for the experiments. There is no background information or introduction. Generally, this paper needs to improve largely.

questions:
As mentioned above, I suggest the authors improve their academic writing skills and design specific application scenarios, such as robotics and transportation, to verify their framework.

I recommend several papers, as shown below, in which authors can learn how to improve academic writing skills and organize corresponding ideas from them.

1) Yang, Q., & Parasuraman, R. Bayesian strategy networks based soft actor-critic learning. ACM Transactions on Intelligent Systems and Technology (TIST).

2) H. Hamann and H. Wo ̈rn, “A framework of space–time continuous models for algorithm design in swarm robotics,” Swarm Intelligence, vol. 2, no. 2-4, pp. 209–239, 2008.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730413003651

thread_type:
official_review

<thread_object>
creator: reviewer_abjt

<content>
summary:
This paper introduces ControlAgent, a framework that automates control system design by integrating large language model (LLM) agents with domain expertise. The framework uses multiple collaborative agents to emulate human iterative design processes, gradually tuning controller parameters to meet user-specified requirements for stability, performance, and robustness. ControlAgent consists of a central agent that analyzes tasks and distributes them to specialized agents, task-specific agents that handle detailed controller design for different system types, a Python computation agent that performs control calculations and evaluations, and a history and feedback module that enables iterative refinement of designs. The system addresses the inherent complexity of control design by breaking down the process into manageable steps and incorporating domain knowledge into the decision-making process. The authors also develop ControlEval, an evaluation benchmark comprising 500 control tasks across various system types including first-order, second-order, systems with delay, and higher-order systems, with different response modes and specific performance criteria. This benchmark serves as a standardized way to evaluate control design workflows.

strengths:
The core strength of this paper lies in how it successfully addresses the fundamental performance-robustness trade-offs inherent in classical control theory. The framework intelligently uses loop-shaping and PID tuning methodologies, employing settling time and phase margin as key tuning parameters - a sophisticated approach that mirrors established control engineering practices. The iterative design process is noteworthy for its theoretical soundness. Rather than treating controller design as a single-shot optimization problem, ControlAgent mimics the systematic approach used by human experts, progressively refining controller parameters while managing the complex interplay between performance metrics. The empirical results validate this approach, showing success across various system types and complexity levels, with particularly impressive results in handling unstable and higher-order systems. The framework's ability to achieve 100% success rates for first-order and stable second-order systems, while maintaining high performance even for complex higher-order and unstable systems, demonstrates its robust theoretical foundation and practical effectiveness.

weaknesses:
- The evaluation methodology raises several concerns. While ControlEval includes 500 control tasks, the paper doesn't clearly justify the distribution of these tasks or demonstrate their representativeness of real-world control problems. The generation process for higher-order systems is particularly problematic - the authors admit to manually designing these cases, which could introduce bias and may not reflect the true complexity of higher-order system control.
- The comparison with baselines is somewhat limited. The paper primarily compares against relatively simple LLM-based approaches (zero-shot, few-shot) and a single traditional tool (PIDtune). Modern control design often employs more complex methods like robust control, model predictive control, or optimization-based approaches, which are notably absent from the comparison. The performance metrics are also relatively basic, focusing mainly on settling time and phase margin while overlooking other important characteristics like disturbance rejection and noise sensitivity.
- The iterative design process lacks theoretical guarantees of convergence or optimality. The paper doesn't provide analysis of when or why the iteration process might fail, nor does it establish bounds on the number of iterations needed for convergence. 
- The framework's heavy reliance on proprietary LLM models raises questions about reproducibility and practical deployment. The authors don't thoroughly explore how the system's performance might vary with different base LLMs or how it might degrade with smaller, more practical models.

questions:
- How does ControlAgent handle model uncertainty? While you discuss robustness through phase margin, could you elaborate on whether the framework considers parametric uncertainties or unmodeled dynamics?
- For higher-order systems, you mention manual design of 50 cases. Could you explain your methodology for ensuring these cases are representative and unbiased? What criteria guided your selection?
- For the history and feedback module, how do you handle the context window limitations of LLMs? Could you provide more details about the memory management strategy?
- Could you provide a more detailed analysis of failure cases, particularly for higher-order systems where performance was lower? Understanding these cases would help assess the framework's limitations.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732505579265

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Response, Part 3

comment:
**Relevance to the ICLR Community**

Reviewer W6Fy has asked about our paper's relevance to ICLR, and we believe our work is particularly relevant to the ICLR community. As stated by the ICLR 2025 Reviewer Guide, a paper brings value to the ICLR community when it convincingly demonstrates new, relevant, impactful knowledge. **A key academic contribution of our paper is the novel integration of two major disciplines - control engineering and large language models.** Control engineering is not merely a domain-specific application, but rather a fundamental discipline that underpins modern technology and civilization (from aerospace and robotics to power systems and autonomous vehicles) and has its own rich mathematical foundations and engineering design principles developed over decades. Our paper presents the first approach to systematically bridging these two disciplines by showing how to encode complex control engineering principles into LLM agents and combine control-oriented computation tools with LLM reasoning to enable ControlAgent to automatically navigate the fundamental performance-robustness tradeoffs in control design. This is beyond a naive integration of control and LLMs. Our key contributions include:

1. Iterative Refinement Mechanism: Mimicking control engineers' decision-making processes to navigate performance-robustness trade-offs.
2. Structured Memory Management: Allowing LLMs to learn from prior designs while respecting context limitations.
3. Multi-Agent Architecture: Enabling specialized LLM agents to collaborate effectively, ensuring technical correctness and efficiency.

 
Similar to how the integration of AI with fields like physics or biology has led to groundbreaking advances (e.g., AlphaFold), our work demonstrates how combining LLMs with control engineering can push both fields forward. The insights gained here are broadly applicable to other engineering disciplines, offering valuable lessons for integrating LLMs with deep engineering domain expertise.

We also align with ICLR’s mission to explore AI applications across domains, including robotics, autonomy, and planning, as stated in the Call for Papers (https://iclr.cc/Conferences/2025/CallForPapers). Previous ICLR papers, such as LLMs for operations research or geospatial tasks [1,2], affirm that engineering applications are highly relevant. Similarly, our work advances ML methodology by enabling LLMs to perform technical reasoning and design in engineering domains requiring both deep theoretical understanding and sharp engineering design intuitions.

Our empirical validation through ControlEval and the new added results on real-world applications, systematically demonstrates ControlAgent’s effectiveness compared to traditional tools and baseline LLM approaches. The strong performance highlights the value of integrating rigorous engineering principles with LLM reasoning, showcasing a practical path toward leveraging AI for real-world, complex problem-solving. We believe our work exemplifies ICLR’s tradition of advancing ML methodology through interdisciplinary innovation. While our paper focuses on control design, the broader implications for LLM-enabled engineering provide valuable insights that extend beyond a single discipline.

[1] Xiao, Z., Zhang, D., Wu, Y., Xu, L., Wang, Y.J., Han, X., Fu, X., Zhong, T., Zeng, J., Song, M. and Chen, G., 2023. Chain-of-Experts: When LLMs Meet Complex Operations Research Problems. ICLR.

[2] Manvi, R., Khanna, S., Mai, G., Burke, M., Lobell, D. and Ermon, S., 2023. Geollm: Extracting geospatial knowledge from large language models. ICLR.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732506582987

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Response, Part 2

comment:
**New Results in Revision for Improving Empirical Soundness**

Reviewer  AbjT gives a really valuable comment on that we need to demonstrate the representativeness of ControlEval for real-world control problems. To address this comment, we have obtained two new important results.

First, we expanded ControlEval with a new application category consisting of 10 real-world applications from diverse resources. In addition, for these 10 real-world physical systems, we randomly generated the design requirements for settling time and phase margin within proper range to reduce human bias. For this category, we just sample the upper bounds of settling time since for real-life applications it is important ensure the fast tracking. The information of these application problems are briefly given below. 

| Real-life application                | Dynamical System                                    | Settling Time       | Phase Margin        |
|--------------------------------------|----------------------------------------------------|-----------------------------------|---------------------------------|
| Laser Printer Positioning System     | $\frac{4(s + 50)}{s^2 + 30s + 200}$               | $T_s \le 0.36$       | $\phi_m \ge 74.24^\circ$       |
| Space Station Orientation Control    | $\frac{20}{s^2 + 20s + 100}$                      | $T_s \le 0.64$        | $\phi_m \ge 76.22^\circ$       |
| Vehicle Steering Control System      | $\frac{1}{s(s+12)}$                               | $T_s \le 0.58$        | $\phi_m \ge 56.98^\circ$       |
| Antenna Azimuth Control System       | $\frac{20.83}{s^2 + 101.7s + 171}$               | $T_s \le 1.57$        | $\phi_m \ge 82.95^\circ$       |
| Autonomous Submersible Control       | $\frac{-0.13(s+0.44)}{s^2 + 0.23s + 0.02}$        | $T_s \le 41.49$     | $\phi_m \ge 69.49^\circ$       |
| Aircraft Pitch Control System        | $\frac{1.151s+0.1774}{s^3+0.739s^2+0.92s+1}$      | $T_s \le 33.58$     | $\phi_m \ge 53.92^\circ$       |
| Missile Yaw Control System           | $\frac{-0.5(s^2+2500)}{(s-3)(s^2+50s+1000)}$      | $T_s \le 3.95$       | $\phi_m \ge 63.43^\circ$       |
| Helicopter Pitch Control System      | $\frac{25(s+0.03)}{(s+0.4)(s^2-0.36s+0.16)}$      | $T_s \le 30.36$     | $\phi_m \ge 66.81^\circ$       |
| Speed Control of a Hard Disk Drive   | $\frac{-0.1808s^4-0.5585s^3+0.4249s^2-8.625s+135.1}{s^4+0.2046s^3+8.932s^2+0.1148s+0.007285}$ | $T_s \le 88.11$     | $\phi_m \ge 52.22^\circ$       |
| High-Speed Train Control System      | $\frac{12}{(s+10)(s+70)}$                         | $T_s \le 2.08$      | $\phi_m \ge 74.40^\circ$       |

 We have obtained the evaluation results for ControlAgent and baseline methods as below:

| Method            | Zero-shot | Zero-shot CoT | Few-shot | Few-shot CoT | PIDTune | ControlAgent |
|--------------------|-----------|---------------|----------|--------------|---------|--------------|
| Success Rate (%)   | 10        | 0             | 20        | 0            | 50      | 100          |

One can clearly see that ControlAgent outperforms all the baselines in this new class of real-world control application problems. More discussions will be provided in the individual response to Reviewer AbjT and included in Appendix C.1 of our revised paper.

Secondly, we evaluated ControlAgent and all the baseline methods on a real DC motor control task in the physical real world (the model used for design is a third-order model in the form of $T(s)=\frac{K }{s\left((L_a s+R_a)(J s+b)+K_\tau K_v \right)}$ but there is a gap between the model and the real motor that the control is deployed). Specifically, we implement the designed controllers from ControlAgent and all the baseline methods on a real DC motor and provide real-world data on the controller performances. The results again demonstrate that ControlAgent gives much better design than all the baseline methods when the designed controllers are deployed on a real system (details will be given in Appendix C.2 of our revised paper).

We believe that our new results have convincingly demonstrated the practical use of ControlAgent across a wide range of real-world control tasks, significantly improving the empirical soundness of our work.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732506670136

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Response, Part 1

comment:
We sincerely thank all the reviewers for the feedback and comments. We start with a common response clarifying the theoretical/empirical soundness of our approach and the relevance of our paper to the ICLR community. We will address all the review comments in a more detailed manner in the individual responses provided for each reviewer. 

**Theoretical Soundness of Our Paper**

As commented by Reviewer  AbjT, the iterative design process of ControlAgent is noteworthy for its theoretical soundness. Our ControlAgent framework is the first LLM agent framework in automating the process of designing controllers that can rigorously meet pre-specified requirements on performance (settling time) and robustness (phase margin) and effectively navigate the fundamental performance/robustness trade-off in classic control design. Reviewer W6Fy is skeptical on whether the overall ControlAgent system can work without a human in the loop or method for filtering correct answers. Here we emphasize that ControlAgent is fully automated and there is no human in the loop. Specifically, ControlAgent uses a Python computation sub-agent to automatically and exactly evaluate the stability, settling time, and phase margin of the designed controllers, and put such exact information into a memory module such that the LLM sub-agents exactly know whether the current designed controller meets the user-specified performance/robustness requirements or not. The Python computation sub-agent is integrated as a module of ControlAgent and works in a fully automated manner to ensure the ControlAgent always know the true settling time and phase margin of its own designed controllers. To summarize, our approach is technically sound in: i) LLM sub-agents are set up to automatically mimic how practicing engineers reason about the tuning of PID or loop-shaping controllers, ii) Python computation sub-agents and the memory module are integrated to ensure that ControlAgent can provide performance/robustness guarantees for its own control design in a fully automated manner.
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper's core strength is its demonstration that LLMs can be used to design controllers for a wide variety of different systems with specific stability, phase margin, and settling times. The system, ControlAgent, runs in a loop where the LLM designed controller is run and the system provides feedback based on the performance of a set of designs. The paper convincingly demonstrates that LLMs can be used for this purpose, and ControlAgent excels across many different control settings with high success rates even when the dynamics are complex and unstable. Designing controllers with machine learning models is a highly relevant application area to ICLR, and knowing that LLMs can do this effectively is a valuable contribution.

However, the main weakness, although not pointed out by reviewers, is the lack of contextualization of ControlAgent in the exceptionally large literature on using machine learning for control problems. There is no section in the related work that addresses the very well researched area on machine learning control (see https://faculty.washington.edu/sbrunton/mlcbook/CH02_MLC.pdf for an introduction). There is similarly no comparison to a baseline which uses a machine learning model *other* than an LLM for controller design. 

Without grounding in this literature, the relevant sub-community at ICLR will not engage with this paper. There is similarly no discussion of what the benefits of an LLM over an alternative ML model are for controller design, which would be very valuable for making the paper more impactful. As a result, this paper cannot be accepted in its current form. Fixing the contextualization with respect to the ML for control community for a future version of the paper will make the paper significantly stronger.