We address the personalization of control systems, which is an attempt to adjust inherent safety and other essential control performance based on each user's personal preferences. A typical approach to personalization requires a substantial amount of user feedback and data collection, which may result in a burden on users. Moreover, it might be challenging to collect data in real-time. To overcome this drawback, we propose a natural language-based personalization, which places a comparatively lighter burden on users and enables the personalization system to collect data in real-time. In particular, we consider model predictive control (MPC) and introduce an approach that updates the control specification using chat within the MPC framework, namely ChatMPC. In the numerical experiment, we simulated an autonomous robot equipped with ChatMPC. The result shows that the specification in robot control is updated by providing natural language-based chats, which generate different behaviors.

翻译：本文研究控制系统的个性化问题，旨在根据每位用户的个人偏好调整系统的固有安全性与关键控制性能。传统的个性化方法通常需要大量用户反馈和数据集收集，这不仅给用户带来负担，还可能面临实时数据采集的困难。为克服这一缺陷，我们提出一种基于自然语言的个性化方法，该方法对用户负担相对较轻，且能使个性化系统实时采集数据。具体而言，我们以模型预测控制（MPC）为对象，提出一种在MPC框架内通过聊天更新控制规范的方法，即ChatMPC。在数值实验中，我们模拟了配备ChatMPC的自主机器人。结果表明，通过提供基于自然语言的聊天输入，机器人控制规范得以更新，并生成了不同的行为模式。