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Govindan, V. and Pappa, N. (2022) Online Learning Based Neural Network Adaptive Controller for Efficient Power Tracking of PWR Type Reactor with Unknown Internal Dynamics. Annals of Nuclear Energy, 168, 108866. https://doi.org/10.1016/j.anucene.2021.108866

has been cited by the following article:

• TITLE: Switching Control Method for Optimal State Feedback Controller of Nuclear Reactor Power System

  AUTHORS: Airan Dang, Bowen Tu, Xiuchun Luan

  KEYWORDS: Optimal Status Feedback, Particle Swarm Optimization, Neural Networks, Controller Switching Control, Load Tracking

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.8, August 31, 2023

  ABSTRACT: Due to the nonlinearity of the reactor power system, the load tracking situation is closely related to the initial steady-state power and the final steady-state power after the introduction of the state feedback controller. Therefore, when the initial power and the final stable power are determined, the particle swarm optimization algorithm is used to find the optimal controller parameters to minimize the load tracking error. Since there are many combinations of initial stable power and final stable power, it is not possible to find the optimal controller parameters for all combinations, so the neural network is used to take the final stable power and the initial stable power as input, and the optimal controller parameters as the output. This method obtains the optimal state feedback controller switching control method can achieve a very excellent load tracking effect in the case of continuous power change, in the power change time point, the response is fast, in the controller parameter switching time point, the actual power does not fluctuate due to the change of controller parameters.