Naimi, A and Deng, J and Shimjith, SR and Arul, J
(2020)
Dynamic Neural Network-based Feedback Linearization Control for a Pressurized Water Reactor.
In: Developments in eSystems Engineering, 14 December 2020 - 17 December 2020, Liverpool, UK (virtual).
(In Press)
Abstract
This note presents a nonlinear control approach using dynamic neural network (DNN)-based feedback linearization (FBL) for nuclear reactor power control. The reactor model adopted in this study is based on neutronic dynamic and thermal-hydraulic models. The nonlinear plant is identified by a single-layer DNN trained using Quasi-Newton and Interior- Point methods. The feedback linearization scheme is combined with a Proportional-Integral (P-I) controller and simulations show good performance of the proposed controller. The efficacy of the controller is evaluated in the load-following mode of operation. Moreover, the fault-tolerance performance of the proposed approach is tested.
More Information
Status: | In Press |
---|---|
Refereed: | Yes |
Depositing User (symplectic) | Deposited by Deng, Jiamei |
Date Deposited: | 05 Feb 2021 15:54 |
Last Modified: | 23 Feb 2022 11:02 |
Event Title: | Developments in eSystems Engineering |
Event Dates: | 14 December 2020 - 17 December 2020 |
Item Type: | Conference or Workshop Item (Paper) |
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Due to copyright restrictions, this file is not available for public download. For more information please email openaccess@leedsbeckett.ac.uk.
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