GenControl: Generative AI-Driven Autonomous Design of Control Algorithms

TL;DR

This paper introduces GenControl, an autonomous framework that combines Large Language Models and optimization algorithms to automate the design of control algorithms for complex systems, improving efficiency and performance.

Contribution

It presents a novel bi-level optimization approach using LLMs and PSO for automated control algorithm design, a significant advancement over traditional manual methods.

Findings

Successfully designed a high-performance adaptive controller for a DC-DC Boost converter.

Achieved controllers that meet criteria for response speed, accuracy, and robustness.

Demonstrated the framework's potential for automating complex control system design.

Abstract

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.