Speaker
Description
As fusion devices move toward reactor-scale conditions, robust, real-time plasma control is essential for sustaining high-performance operation and tight stability margins. This talk presents recent AI-driven control advances on the DIII-D and KSTAR tokamaks, highlighting successful demonstrations of RMP-induced ELM control [1], preemptive tearing mode suppression [2], and radiation-front-based detachment control [3,4]. To make these control architectures resilient for Future Pilot Plants (FPP), we introduce foundational AI-driven approaches that further strengthen system reliability against diagnostic degradation and actuator failures. Advanced plasma monitoring tools like Diag2Diag [5] and real-time kinetic equilibrium reconstruction (rt-CAKE) [6] provide fault-tolerant virtual diagnostics and continuous state evaluation. Alongside robust plasma monitoring, efficient actuator coordination is equally crucial. Complementing this need, neural network surrogates such as TORBEAM-NN [7] enable fast, scalable, real-time electron cyclotron heating (ECH) optimization, even during gyrotron loss. Together, integrating these high-fidelity diagnostics and adaptive actuator models establishes a crucial pathway toward intelligent, failure-tolerant tokamak operation.