Speaker: Prof. Sadayoshi Murakami, Kyoto University
Abstract: We develop a model predictive control system for fusion plasmas based on data assimilation, which integrates predictive model (digital twin) adaptation using real-time measurements and control estimation robust against model and observation uncertainties. The main part of the control system, ASTI, predicts the probability distribution of future plasma states and estimates the optimal control input and the actual plasma state based on Bayes' theorem. In this study, the ASTI-centered control system has been implemented in the Large Helical Device (LHD) and successfully applied to control the electron and ion temperatures and electron density. The control experiments demonstrate the effectiveness of the data assimilation-based control approach, which allows the synergistic interaction of measurement, heating, fueling, and simulation. This approach can provide a flexible platform for the digital twin control of future fusion reactors.