LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models

• URL: http://arxiv.org/abs/2507.20509v1
• Date: Mon, 28 Jul 2025 04:12:43 GMT
• Title: LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models
• Authors: Zhongchao Zhou, Yuxi Lu, Yaonan Zhu, Yifan Zhao, Bin He, Liang He, Wenwen Yu, Yusuke Iwasawa,
• Abstract summary: Large Language Models (LLMs) are increasingly applied in robotics.<n>We propose an LLM-guided adaptive compensator framework that avoids designing controllers from scratch.<n>This study opens a new direction for applying LLMs in the field of automatic control.
• Score: 22.989496527440636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With rapid advances in code generation, reasoning, and problem-solving, Large Language Models (LLMs) are increasingly applied in robotics. Most existing work focuses on high-level tasks such as task decomposition. A few studies have explored the use of LLMs in feedback controller design; however, these efforts are restricted to overly simplified systems, fixed-structure gain tuning, and lack real-world validation. To further investigate LLMs in automatic control, this work targets a key subfield: adaptive control. Inspired by the framework of model reference adaptive control (MRAC), we propose an LLM-guided adaptive compensator framework that avoids designing controllers from scratch. Instead, the LLMs are prompted using the discrepancies between an unknown system and a reference system to design a compensator that aligns the response of the unknown system with that of the reference, thereby achieving adaptivity. Experiments evaluate five methods: LLM-guided adaptive compensator, LLM-guided adaptive controller, indirect adaptive control, learning-based adaptive control, and MRAC, on soft and humanoid robots in both simulated and real-world environments. Results show that the LLM-guided adaptive compensator outperforms traditional adaptive controllers and significantly reduces reasoning complexity compared to the LLM-guided adaptive controller. The Lyapunov-based analysis and reasoning-path inspection demonstrate that the LLM-guided adaptive compensator enables a more structured design process by transforming mathematical derivation into a reasoning task, while exhibiting strong generalizability, adaptability, and robustness. This study opens a new direction for applying LLMs in the field of automatic control, offering greater deployability and practicality compared to vision-language models.

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