LQG/LTR Controller Design Based on Improved SFACC for the PWR Reactor Power Control System

Wan, Jiashuang; Wang, Pengfei

doi:10.1080/00295639.2019.1710419

Cited by 15 publications

(11 citation statements)

References 11 publications

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“…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%

“…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]. In the SFAC framework, the linear quadratic Gaussian (LQG) technique has been integrated with the loop transfer recovery (LTR) scheme to improve temperature and power control during reactor parameters variations [2]- [6]. To control reactor power with enhanced robustness as compared to the conventional LQG control, H ∞ based techniques have been suggested [7]- [10].…”

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: Introductionmentioning

confidence: 99%

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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“…A considerable amount of research has been done on the application of robust control techniques in nuclear reactors, especially for the core power control. In the last two decades, various control design techniques such as state feedback assisted control (SFAC) [1]- [5], H ∞ control [6]- [9], model predictive control (MPC) [10]- [14], sliding mode control (SMC) [15]- [21], and soft-computing based controls [22]- [27] have been proposed to deal with disturbances and uncertainties in NPPs. Edwards et al [1] proposed the idea of SFAC to enhance the stability of the classical control loop by integrating a state-feedback compensating loop.…”

mentioning

confidence: 99%

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

Vajpayee¹,

Becerra²,

Bausch³

et al. 2021

Progress in Nuclear Energy

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mentioning

confidence: 99%

“…Edwards et al [1] proposed the idea of SFAC to enhance the stability of the classical control loop by integrating a state-feedback compensating loop. Loop transfer recovery (LTR) technique has been combined with linear quadratic Gaussian (LQG) control in an SFAC framework for the improvement of reactor temperature and power controls during variation in reactor parameters [2]- [5]. H ∞ -based control schemes have been designed for reactor power control and to obtain enhanced robustness over the classical LQG control scheme [6]- [9].…”

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confidence: 99%

Failure Modes Detection of Nuclear Systems Using Machine Learning

Tian¹,

Deng²,

Zio³

et al. 2018

2018 5th International Conference on Dependable Systems and Their Applications (DSA)

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A control design scheme is formulated for a pressurized water type nuclear power plant by integrating the optimal linear quadratic Gaussian (LQG) control with the robust integral sliding mode (ISM) technique. A novel robust-optimal hybrid control scheme is further proposed by integrating the LQG-ISM technique with the loop transfer recovery approach to enhance the effectiveness and robustness capability. The control architecture offers robust performance with minimum control efforts and tracks the reference set-point effectively in the presence of disturbances. The multi-input-multi-output nuclear power plant model adopted in this work is characterized by 40 state variables. The nonlinear plant model is linearised around steady-state operating conditions to obtain a linear model for controller design. The efficacy of proposed controllers is demonstrated by simulations on different subsystems of the nuclear power plant. The control performance of the proposed technique is compared with other classical control design schemes. Statistical measures are employed to quantitatively analyse and compare the performance of the different controllers that are studied in the work.

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LQG/LTR Controller Design Based on Improved SFACC for the PWR Reactor Power Control System

Cited by 15 publications

References 11 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

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

Failure Modes Detection of Nuclear Systems Using Machine Learning

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