  To advance the paradigm of autonomous operation for nuclear power plants, a
data-driven machine learning approach to control is sought. Autonomous
operation for next-generation reactor designs is anticipated to bolster safety
and improve economics. However, any algorithms that are utilized need to be
interpretable, adaptable, and robust.
  In this work, we focus on the specific problem of optimal control during
autonomous operation. We will demonstrate an interpretable and adaptable
data-driven machine learning approach to autonomous control of a molten salt
loop. To address interpretability, we utilize a data-driven algorithm to
identify system dynamics in state-space representation. To address
adaptability, a control algorithm will be utilized to modify actuator setpoints
while enforcing constant, and time-dependent constraints. Robustness is not
addressed in this work, and is part of future work. To demonstrate the
approach, we designed a numerical experiment requiring intervention to enforce
constraints during a load-follow type transient.
