Real‐time estimation of the electron temperature profile in DIII‐D by leveraging neural‐network surrogate models

Abstract Control of both the magnitude and the shape of tokamak profiles will be necessary to achieve stable, high‐performance plasmas. In order to reject disturbances in real time, feedback‐control algorithms rely on accurate real‐time knowledge of the plasma state. When diagnostics alone are insufficient, because they are limited in number or their measurements are too noisy, observers can be used to combine diagnostic data with a response model to provide a better estimation of different plasma properties. An observer has been developed to estimate the electron temperature profile in real time using both diagnostic data from the Thomson scattering system and a model based on the electron heat transport equation describing the evolution of the electron temperature profile. Neural network surrogate models are leveraged to help improve the overall model prediction while staying within computation time constraints for real‐time use. The observer algorithm is shown in offline tests to produce smooth profiles that are consistent with both the diagnostic data and the electron heat transport equation. When implemented into the real‐time plasma control system, this observer will provide valuable information on the electron temperature profile to many potential feedback‐control applications.