AGI-Based Control of Nuclear Fusion Using a Universal Balance–Feedback Framework

Abstract—This paper proposes a unified theoretical and computational framework for controlling nuclear fusion systems using Artificial General Intelligence (AGI). The framework is based on a universal principle that all complex systems remain stable through dynamic balance, continuous feedback, and minimization of defects. By integrating this principle with plasma physics, control theory, and machine learning, we present an adaptive architecture capable of sustaining fusion reactions through real-time optimization. A scalar stability index S(t) derived from normalized plasma parameters serves as both a physics-grounded performance metric and a reward signal for reinforcement-learning-based control. Simulation results demonstrate convergence of S(t) to a target operating regime under the proposed AGI controller.