Nonlinear model predictive control using feedback linearization for a pressurized water nuclear power plant

Amine Naimi, Jiamei Deng, Vineet Vajpayee, Victor Becerra, S. R. Shimjith, A. John Arul

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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 analyse the performances of the two control strategies.

Original languageEnglish
Pages (from-to)16544-16555
JournalIEEE Access
Early online date7 Feb 2022
Publication statusPublished - 15 Feb 2022


  • Coolants
  • Dynamic neural network
  • feedback linearization
  • Fuels
  • Inductors
  • Load modeling
  • Mathematical models
  • model predictive control
  • Neural networks
  • nuclear power plant
  • pressurized water reactor
  • Turbines
  • UKRI
  • EP/R021961/1
  • EP/R022062/1


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