  Advanced reactors to be deployed in the coming decades will face deregulated
energy markets, and may adopt flexible operation to boost profitability. To aid
in the transition from baseload to flexible operation paradigm, autonomous
operation is sought. This work focuses on the control aspect of autonomous
operation. Specifically, a hierarchical control system is designed to support
constraint enforcement during routine operational transients. Within the
system, data-driven modeling, physics-based state observation, and classical
control algorithms are integrated to provide an adaptable and robust solution.
A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design
basis for demonstrating the control system.
  The hierarchical control system consists of a supervisory layer and low-level
layer. The supervisory layer receives requests to change the system's operating
conditions, and accepts or rejects them based on constraints that have been
assigned. Constraints are issued to keep the plant within an optimal operating
region. The low-level layer interfaces with the actuators of the system to
fulfill requested changes, while maintaining tracking and regulation duties. To
accept requests at the supervisory layer, the Reference Governor algorithm was
adopted. To model the dynamics of the reactor, a system identification
algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution
of process variables that cannot be directly measured, the Unscented Kalman
Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The
composition of these algorithms led to a numerical demonstration of constraint
enforcement during a 40 % power drop transient. Adaptability was demonstrated
by modifying the constraint values, and enforcing them during the transient.
Robustness was demonstrated by enforcing constraints under noisy environments.
