The static thermal environment is unfavorable to the human's health as it can reduce the ability of human’s heat adaptation. The dynamic thermal environment is favorable to the human's health as it is similar to the natural environment. This paper presents a dynamical thermal comfortable control system for the inhabited environment with users’ learning. The thermal comfort predicted mean vote (PMV) index is the control aim of the system. The fuzzy learning algorithm of personal thermal comfort zone is proposed that can modify the personal thermal comfort zone on line to meet the needs of different humans. The dynamical thermal comfort control strategy is proposed with computational experiment. The dynamical thermal comfort zone includes comfort zone and energy saving zone which change periodically. The experiment results demonstrate that this method can meet the human’s thermal comfort need and reduce the energy consumption, whilst it is favorable to the human's health.
