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