# GenControl: Generative AI-Driven Autonomous Design of Control Algorithms

arXiv: https://arxiv.org/abs/2506.12554 · HTML: https://mlsanity.com/paper/2506.12554/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges](https://mlsanity.com/paper/2511.14478/) (2025)
- [Perron--Frobenius Operator Matching for Generative Modeling](https://mlsanity.com/paper/2606.17465/) (2026)

## Abstract (arXiv)

Designing controllers for complex industrial electronic systems is challenging due to nonlinearities and parameter uncertainties, and traditional methods are often slow and costly. To address this, we propose a novel autonomous design framework driven by Large Language Models (LLMs). Our approach employs a bi-level optimization strategy: an LLM intelligently explores and iteratively improves the control algorithm's structure, while a Particle Swarm Optimization (PSO) algorithm efficiently refines the parameters for any given structure. This method achieves end-to-end automated design. Validated through a simulation of a DC-DC Boost converter, our framework successfully evolved a basic controller into a high-performance adaptive version that met all stringent design specifications for fast response, low error, and robustness. This work presents a new paradigm for control design that significantly enhances automation and efficiency.