Abstract
Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temperature plasma within the tokamak vessel. This requires high-dimensional, high-frequency, closed-loop control using magnetic actuator coils, further complicated by the diverse requirements across a wide range of plasma configurations. In this work, we introduce a previously undescribed architecture for tokamak magnetic controller design that autonomously learns to command the full set of control coils. This architecture meets control objectives specified at a high level, at the same time satisfying physical and operational constraints. This approach has unprecedented flexibility and generality in problem specification and yields a notable reduction in design effort to produce new plasma configurations. We successfully produce and control a diverse set of plasma configurations on the Tokamak à Configuration Variable1,2, including elongated, conventional shapes, as well as advanced configurations, such as negative triangularity and ‘snowflake’ configurations. Our approach achieves accurate tracking of the location, current and shape for these configurations. We also demonstrate sustained ‘droplets’ on TCV, in which two separate plasmas are maintained simultaneously within the vessel. This represents a notable advance for tokamak feedback control, showing the potential of reinforcement learning to accelerate research in the fusion domain, and is one of the most challenging real-world systems to which reinforcement learning has been applied.
Highlights
The conventional approach to this time-varying, non-linear, multivariate control problem is to first solve an inverse problem to precompute a set of feedforward coil currents and voltages[7,8]
A radically new approach to controller design is made possible by using reinforcement learning (RL) to generate non-linear feedback controllers
We demonstrate the effectiveness of our controllers in experiments carried out on the Tokamak à Configuration Variable (TCV)[1,2], in which we demonstrate control of a variety of plasma shapes, including elongated ones, such as those foreseen in ITER, as well as advanced configurations, such as negative triangularity and ‘snowflake’ plasmas
Summary
We present a new paradigm for plasma magnetic confinement on tokamaks. Our control design fulfils many of the hopes of the community for a machine-learning-based control approach[14], including high performance, robustness to uncertain operating conditions, intuitive target specification and unprecedented versatility. A novel plasma position and shape controller for advanced configuration development on the TCV tokamak. L. Real time control of a tokamak plasma using neural networks. Predicting disruptive instabilities in controlled fusion plasmas through deep learning. Development of Free-boundary Equilibrium and Transport Solvers for Simulation and Real-time Interpretation of Tokamak Experiments. D. et al Real-time optical plasma boundary reconstruction for plasma position control at the TCV Tokamak. H. et al Real time magnetic control of the snowflake plasma configuration in the TCV tokamak. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
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