Associative stochastic automaton for reactor power ascent

The dynamics of experimental nuclear reactors exhibit strong non-linearities, while the constraints associated with their operation embody a number of safety-related decision points and protective trips. For power maneuvering, existing control technologies include human operators, state variable feedback controllers, and digital model-based controllers. In this work, we demonstrate the use of an associative stochastic automaton as an alternative. Factors which lead to the success of the application are discussed