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Adaptive critic-based neurofuzzy controller for the steam generator water level

Fakhrazari, A ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1109/TNS.2008.924058
  3. Publisher: 2008
  4. Abstract:
  5. 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. © 2008 IEEE
  6. Keywords:
  7. Backpropagation algorithms ; Control theory ; Descaling ; Disturbance rejection ; Dynamic programming ; Electric power plants ; Feedback ; Fuzzy inference ; Fuzzy rules ; Inference engines ; Laws and legislation ; Mathematical programming ; Military data processing ; Nonlinear systems ; Nuclear energy ; Nuclear industry ; Nuclear power plants ; Power plants ; Reinforcement ; Reinforcement learning ; Robust control ; State feedback ; Steam ; Steam generators ; Steam power plants ; Systems engineering ; Uncertainty analysis ; Water levels ; Adaptive critics ; Controller designs ; Design procedures ; Error algorithms ; Fuzzy inference rules ; Fuzzy rule base (FRB) ; Highly nonlinear ; Inverse response ; Low operating power (LOP) ; Model uncertainties ; Neuro fuzzy controllers ; Nuclear steam generators ; Reinforcement learning (RL) methods ; System feedback ; Transient responses ; Fuzzy logic
  8. Source: IEEE Transactions on Nuclear Science ; Volume 55, Issue 3 , 2008 , Pages 1678-1685 ; 00189499 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/4545112