Title :
Adaptive Critic-based Neurofuzzy Controller for the Steam Generator Water Level
Author :
Fakhrazari, Amin ; Boroushaki, Mehrdad
Author_Institution :
Dept. of Mech. Eng., Sharif Univ. of Technol., Tehran
fDate :
6/1/2008 12:00:00 AM
Abstract :
In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts of the fuzzy inference rules. The critic agent here has a proportional-derivative structure and the fuzzy rule base has nine rules. The proposed controller shows satisfactory transient responses, disturbance rejection and robustness to model uncertainty. Its simple design procedure and structure, nominates it as one of the suitable controller designs for the steam generator water level control in nuclear power plant industry.
Keywords :
PD control; adaptive control; dynamic programming; fuzzy control; nuclear power stations; nuclear reactor steam generators; adaptive critic-based neurofuzzy controller; dynamic programming; fuzzy critic-based learning; fuzzy inference rules; nuclear power plant industry; nuclear steam generators; proportional-derivative structure; reinforcement learning method; steam generator water level control; water level regulation; Adaptive control; Dynamic programming; Learning; Nonlinear dynamical systems; Nonlinear systems; Nuclear facility regulation; Nuclear power generation; Power generation; Programmable control; State feedback; Adaptive critic-based design; fuzzy logic; reinforcement learning; vertical U-tube steam generator;
Journal_Title :
Nuclear Science, IEEE Transactions on
DOI :
10.1109/TNS.2008.924058