Robust-optimal integrated control design technique for a pressurized water-type nuclear power plant

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

doi:10.1016/j.pnucene.2020.103575

Cited by 18 publications

(17 citation statements)

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“…To avoid duplication of work here, a brief summary of the pressurizer model is given. For a detailed description of the studied NPP model, various model equations, plant parameters, and steady-state values of different variables the readers are kindly referred to [24], [48].…”

Section: Pwr Nuclear Power Plantmentioning

confidence: 99%

“…where p p is pressurizer pressure and l w is pressurizer level. The rest of the variables are defined in [24], [48].…”

Section: A Pressurizer Modelmentioning

confidence: 99%

“…Several control design techniques, for example, state feedback assisted control (SFAC) [1]- [6], H ∞ control [7]- [10], model predictive control (MPC) [11]- [16], sliding mode control (SMC) [17]- [24], and different soft computing techniquebased controls [25]- [31] have been proposed in the last two decades to handle uncertainties and disturbances. In the earliest work, the notion of an SFAC has been suggested to boost the local stability of a conventional control by adding a state-feedback compensation [1].…”

Section: Introductionmentioning

confidence: 99%

“…Robust MPC techniques have been developed to handle various constraints in an uncertain NPP system [11]- [16]. Robust control design techniques using SMC are proposed to guarantee robustness towards uncertainties coming from the input channel [17]- [24]. The aforementioned model-based approaches require an error-free mathematical representation of the NPP a priori for an effective control design.…”

Section: Introductionmentioning

confidence: 99%

“…Practically, it is relevant to establish control schemes for the entire NPP system. Nevertheless, a small number of studies are available in the literature to control an entire NPP [24], [42], [46], [47]. On the other hand, many works assumed that all NPP state variables are accessible for control design.…”

Section: Introductionmentioning

confidence: 99%

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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: Pwr Nuclear Power Plantmentioning

confidence: 99%

“…where p p is pressurizer pressure and l w is pressurizer level. The rest of the variables are defined in [24], [48].…”

Section: A Pressurizer Modelmentioning

confidence: 99%