Abstract
This work proposes adaptive control-based design strategies to control a pressurized water reactor (PWR) nuclear power plant (NPP). An {L_{1}} -adaptive-based state-feedback control technique is proposed using the linear quadratic Gaussian control and projection-based adaptation laws. The control scheme possesses good robustness capabilities in handling disturbances and uncertainties. A robust {L_{1}} -adaptive control technique is also proposed by combining the {L_{1}} -adaptive control with the loop transfer recovery (LTR) technology. The framework hence gives the strengthened robust set-point tracking performance given the matched and unmatched uncertainties and disturbances. The NPP model employed in this article is defined by five inputs, five outputs, and 38 state variables. A linear model for controller design is obtained by linearizing the nonlinear NPP model at operating conditions. Various simulations are carried out on subsystems of the NPP to verify the effectiveness of the proposed scheme. Numerical and statistical measures are computed for quantitative analysis of the controllers' performance. Several classical control design techniques are also implemented, and their performance is compared with the proposed adaptive control techniques.
More Information
Identification Number: | https://doi.org/10.1109/TNS.2021.3090526 |
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Status: | Published |
Refereed: | Yes |
Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Uncontrolled Keywords: | 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics, 0299 Other Physical Sciences, 0903 Biomedical Engineering, Nuclear & Particles Physics, |
Depositing User (symplectic) | Deposited by Deng, Jiamei |
Date Deposited: | 29 Sep 2021 09:34 |
Last Modified: | 11 Jul 2024 01:46 |
Item Type: | Article |
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