Transformer-based large language models (LLMs) have achieved remarkable success across natural language processing tasks, and their abilities have been exploited also for dynamic system control, particularly for robots. However, their practical deployment faces critical limitations: reliance on remote servers, substantial computational requirements, and high energy consumption make their practical application to real-time control very challenging. Small language models (SLMs), characterized by significantly fewer parameters, offer a promising alternative by enabling local execution, preserving privacy, and facilitating customization. Despite these advantages, SLMs remain underexplored for dynamic system control. This paper investigates whether SLMs can effectively control dynamic systems in real-time within a model-predictive control (MPC) framework. We propose a novel approach that leverages natural language prompting and a self-assessment strategy to generate effective cost functions for MPC, ensuring real-time adaptability and robustness. Unlike typical existing reward-shaping methods, our methodology is designed to be applicable to arbitrary systems and goals. Through extensive simulations, we test the methodology under diverse dynamic systems and tasks, validating the feasibility of SLM-based real-time control.
Caccia et al. (Fri,) studied this question.