Gain Scheduled Subspace Predictive Control of a Pressurized Water-type Nuclear Reactor

Vajpayee, Vineet; Becerra, Victor M.; Bausch, Nils; Banerjee, Shohan; Deng, Jiamei; Shimjith, S. R.; Arul, A. John

doi:10.1109/med48518.2020.9182853

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The response and the performance of an NPP differ significantly over its lifespan due to parameter variations, unmodelled dynamics, model-plant mismatch, mechanical vibrations, fuel burn-up and refuelling operations, transmutation of structural materials, hydrogen production and diffusion, and wear and tear of equipments. Thus, a constant gain control strategy designed during the commissioning of an NPP may not be suitable at a later operation stage, or it may even cause an undesirable, unpredictable, or unstable response [32]. The necessity of a precise mathematical model becomes rigid, mostly during retrofitting of advanced controllers in an aged NPP.…”

Section: Introductionmentioning

confidence: 99%

L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant

Vajpayee

Becerra

Bausch

et al. 2021

IEEE Trans. Nucl. Sci.

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This work proposes adaptive control-based design strategies to control a pressurized water (PWR) nuclear power plant (NPP). An L1-adaptive-based state feedback control technique is proposed using linear quadratic Gaussian control and projection-based adaptation laws. The control scheme possesses good robustness capabilities in handling disturbances and uncertainties. A Robust L1-adaptive control technique is also proposed by combining the L1-adaptive control with the loop transfer recovery (LTR) technology. The framework hence gives strengthened robust set-point tracking performance given the matched and unmatched uncertainties and disturbances. The NPP model employed in the current work is defined by five-inputs, fiveoutputs, and thirty-eight 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.

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Section: Introductionmentioning

confidence: 99%

L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant

Vajpayee

Becerra

Bausch

et al. 2021

IEEE Trans. Nucl. Sci.

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Online learning based neural network adaptive controller for efficient power tracking of PWR type reactor with unknown internal dynamics

Venkatesan

Pappa

2022

Annals of Nuclear Energy

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

et al. 2022

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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.

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Gain Scheduled Subspace Predictive Control of a Pressurized Water-type Nuclear Reactor

Cited by 3 publications

References 18 publications

L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant

L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant

Online learning based neural network adaptive controller for efficient power tracking of PWR type reactor with unknown internal dynamics

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

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