Model-free stabilization via Extremum Seeking using a cost neural estimator

In this paper, a fully model-free architecture for vertical stabilization of thermonuclear plasmas in tokamak experimental reactors is presented. For the first time, an Extremum Seeking control algorithm is combined with neural networks to estimate the Lyapunov function to be minimized, resulting in a fully data-driven control architecture. The performance of different neural networks are compared. Specifically, Multilayer Perceptrons and Extreme Learning Machines are considered. The proposed architecture is tested in simulation to show that it can counteract relevant plasma disturbances, resulting in a significant improvement in terms of the achievable operative space compared to the Extremum Seeking algorithm, which still relies on model-based cost estimator. • An innovative fully model-free approach to the Vertical Stabilization problem. • Extremum Seeking combined with a neural network estimation of the cost function. • Control gain event-driven adaptive logic for efficient power supply management. • Comparison of performance and computational complexity of neural networks. • Enlarged operative space without tailoring the gains for specific scenarios.