Vajpayee, V. orcid.org/0000-0003-1179-7118, Becerra, V., Bausch, N. et al. (3 more authors) (2021) LQGI/LTR based robust control technique for a pressurized water nuclear power plant. Annals of Nuclear Energy, 154. 108105. ISSN 0306-4549
Abstract
This work proposes a new hybrid control strategy for a pressurized water type nuclear power plant by integrating linear quadratic integrator (LQI), linear quadratic Gaussian (LQG), and loop transfer recovery (LTR) approaches. The multi-input multi-output nuclear plant model adopted in this work is characterized by 38 state variables. The nonlinear plant model is linearized around steady-state operating conditions to obtain a linear model for the controller design. The proposed LQGI/LTR technique designs state-feedback assisted output control using the estimated states. The control architecture offers robust performance and tracks the reference set-point with zero steady-state error in the presence of uncertainties and disturbances. The effectiveness of the proposed technique is demonstrated by simulations on different subsections of a pressurized water nonlinear nuclear power plant model. The control performance of the proposed technique is further compared with other classical control design schemes. Statistical measures are employed to quantitatively analyse control performance.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. This is an author produced version of a paper subsequently published in Annals of Nuclear Energy. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Optimal control; Robust control; Hybrid control; Pressurized water reactor; Nuclear power plant |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Sep 2022 12:42 |
Last Modified: | 05 Sep 2022 12:42 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.anucene.2020.108105 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190545 |