Optimal control is a powerful method for addressing the challenges posed by dynamic systems, such as those found in continuous production. However, many real-world optimization tasks involve solving non-convex optimization problems, which is usually very time-consuming. In previous study, the time consuming problem tackled by an Adaptive Resonance Theory-2 Neural Network (ART-2 NN) employed as a solution technique of optimal control problem.  This study investigates the behavior of real-time, optimal control-based production systems in response to two disturbances: changes in demand and variations in the available machine composition.  LABVIEW is used to create a real-time simulation.  The results demonstrate that the optimal control model exhibits a fast transition time before reaching new states.