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：我们针对控制系统的个性化问题展开研究，旨在根据每个用户的个人偏好调整系统固有安全性及其他关键控制性能。典型的个性化方法需要大量用户反馈和数据收集，这可能给用户带来负担。此外，实时数据收集也可能面临挑战。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收集数据。具体而言，我们聚焦于模型预测控制（MPC），并提出一种在MPC框架内通过聊天更新控制规格的方法，即ChatMPC。在数值实验中，我们模拟了搭载ChatMPC的自主机器人。结果表明，通过提供基于自然语言的聊天内容，机器人控制中的规格得以更新，从而产生不同的行为。