This work presents an end-to-end digital-twin framework that integrates Large Language Models (LLMs) directly into the control loop of a physical asset. By coupling physics-based, data-driven, and hybrid models with natural-language reasoning, the framework bridges human intent, model prediction, and actuation in real time. The asset incorporates physics-based, data-driven, and hybrid predictive models to compare accuracy, generalization, and computational efficiency. On the control side, three complementary strategies are explored: model predictive control, reinforcement learning trained in the digital twin and deployed on the physical asset, and LLM-based controllers capable of interpreting natural-language objectives. The LLM controllers demonstrate autonomous, explainable control decisions and the ability to handle dynamic operating conditions without manual tuning or explicit domain expertise. The key message is conceptual: when LLMs are embedded into scientific workflows, they act as cognitive bridges that allow researchers to explore, model, and control systems in fields where they possess little or no prior knowledge. This paradigm redefines scientific practice-transforming experimentation into a conversational, adaptive, and inclusive process. The implications extend far beyond greenhouse control: such frameworks can democratize scientific discovery, accelerate innovation across disciplines, and open entirely new research horizons where human creativity and machine intelligence collaborate seamlessly.