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  5. VOL 48, NO 3 (2026)
  6. pp. 03-17

Adaptive Human-Machine Systems with Automated Control of Large Language Models for Unmanned Aerial Vehicle Control

D. Vasyk, V. Kharchenko

Èlektron. model. 2026, 48(3):03-17

ABSTRACT

An Intelligent Assistance Systems (IAS) study was conducted on the integration of Large Language Models (LLMs) into control loops for a safety-critical systems context, specifically Unmanned Aerial Vehicles (UAVs) performing relevant missions. The key drawbacks of using generative artificial intelligence in operational control tasks were analyzed, among which the stochastic nature of the models, susceptibility to hallucinations, and insufficient predictability of responses were highlighted. To address the identified issues, a method for organizing adaptive automatic control is proposed, implemented as a separate architectural layer of the AI system. This approach ensures deterministic filtering of the language modelʼs output data, enabling the safe transformation of LLM results into clear executive commands. A set of metrics for analyzing system performance indicators and determining the limits of LLM capabilities in interpreting commands and context was identified.

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KEYWORDS

large language models, human-machine interaction, safety-critical systems, unmanned aerial vehicles, finite-state machines.

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Received 23.02.2026