arXiv subjects

Cristian Galperti

Publications and source records attributed to Cristian Galperti.

4 recordsLinked to original sources

arXiv2026

An Inverse Grad-Shafranov Neural Network Approach to Tokamak Magnetic Control

A new approach to tokamak magnetic control enabling high-precision plasma shaping and novel real-time adaptability is experimentally demonstrated on the Tokamak a Configuration Variable (TCV). The method is motivated by the insight that, under appropriate assumptions, a real-time inverse Grad-Shafranov solver approximates an optimal control policy for plasma boundary regulation. Building on this, a control architecture is developed in which classical controllers enforce operational constraints while a fast surrogate model provides a real-time inverse mapping from the desired plasma boundary to Poloidal Field Coil currents. Experimental results on TCV demonstrate improved plasma shaping with respect to the standard discharge preparation procedure --- albeit without explicit real-time shape feedback --- while enabling flexible response to asynchronous events. It is shown that a single network provides satisfactory performance across a range of plasma magnetic configurations. Real-time adaptivity is demonstrated in simulation, and partially in experiment, through adaptive strike point motion and early termination in response to a real-time trigger. These results suggest a viable path toward magnetic control architectures that reduce reliance on dense diagnostic coverage while maintaining high-accuracy plasma shaping, with potential relevance for future fusion power plant operation.

physics.plasm-ph↗

arXiv2025

Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM's ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

physics.plasm-ph↗

arXiv2025

First experimental demonstration of plasma shape control in a tokamak through Model Predictive Control

In this work, a Model Predictive Controller (MPC) is proposed to control the plasma shape in the Tokamak à Configuration Variable (TCV). The proposed controller relies on models obtained by coupling linearized plasma response models, derived from the \texttt{fge} code of the Matlab EQuilibrium toolbox (MEQ) suite, with a state-space description of the core TCV magnetic control system. It optimizes the reference signals fed to this inner control loop in order to achieve the desired plasma shape while also enforcing constraints on the plant outputs. To this end, a suitable Quadratic Programming (QP) problem is formulated and solved in real-time. The effectiveness of the proposed controller is illustrated through a combination of simulations and experimental results. To the best of our knowledge, this is the first time that a plasma shape control solution based on MPC has been experimentally tested on a real tokamak.

physics.plasm-ph↗

arXiv2023

Towards practical reinforcement learning for tokamak magnetic control

Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic confinement. In this work, we address key drawbacks of the RL method; achieving higher control accuracy for desired plasma properties, reducing the steady-state error, and decreasing the required time to learn new tasks. We build on top of \cite{degrave2022magnetic}, and present algorithmic improvements to the agent architecture and training procedure. We present simulation results that show up to 65\% improvement in shape accuracy, achieve substantial reduction in the long-term bias of the plasma current, and additionally reduce the training time required to learn new tasks by a factor of 3 or more. We present new experiments using the upgraded RL-based controllers on the TCV tokamak, which validate the simulation results achieved, and point the way towards routinely achieving accurate discharges using the RL approach.

physics.plasm-ph↗