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Nonlinear Model Predictive Control Using Feedback Linearization for a Pressurized Water Nuclear Power Plant

Authors :
Amine Naimi
Jiamei Deng
Vineet Vajpayee
Victor Becerra
S. R. Shimjith
A. John Arul
Source :
IEEE Access, Vol 10, Pp 16544-16555 (2022)
Publication Year :
2022
Publisher :
IEEE, 2022.

Abstract

The present work aims to introduce a nonlinear control scheme that combines intelligent feedback linearization (FBL) and a model predictive control (MPC) for a pressurized water reactor (PWR). The nonlinear plant model that is considered in this study is described by the first-principles approach, and it consists of 38 state variables. First, system identification using a dynamic neural network (DNN) structure is performed to obtain a standard affine nonlinear system. The quasi-Newton algorithm is employed to find the best DNN model. Then, an FBL is formulated to address the nonlinearity of the DNN model. An MPC controller is developed based on the FBL system to improve the system performance. The designed controller is compared with a linear MPC controller that is based on state-space models to evaluate the performance of the proposed controller. The proposed approach improves the load-following operation and offers better disturbance rejection capability than the conventional MPC. In addition, numerical measures are employed to compare and analyze the performances of the two control strategies.

Details

Language :
English
ISSN :
21693536 and 61865907
Volume :
10
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.72d6a9ec107f4e61865907141385724d
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2022.3149790