Reactivity control plays an important role in ensuring the safe operation of a nuclear power plant. However, no physical sensor is available to measure it. It can be inferred indirectly either from the reactor period ...
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In the nuclear power plants (NPPs), fault detection and diagnosis (FDD) methods are very important to improve the safety and reliability of plants. Researchers have established various FDD methods such as model-based ...
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This work combines the subspace predictive control technique with the integral sliding mode control strategy to formulate a novel robust subspace predictive control scheme. The subspace predictive controller provides ...
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This work presents a disturbance observer-based predictive control strategy using a subspace matrix structure. The aim is to improve the capability of classical predictive controllers in handling external disturbances...
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This paper presents a generalized extended state observer based-integral sliding mode control for a nuclear reactorsystem subject to mismatched uncertainties. A generalized extended state observer is introduced to es...
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This paper presents a generalized extended state observer based-integral sliding mode control for a nuclear reactorsystem subject to mismatched uncertainties. A generalized extended state observer is introduced to estimate system states as well as the mismatched uncertainties, which is then used to design an integral sliding mode control to eliminate the effect of unknown mismatched uncertainties. Stability of the proposed controller is proved by defining a Lyapunov function. Performance of the proposed control scheme is compared with the conventional integral sliding mode control. Simulation results exhibit that the proposed control scheme has better disturbance rejection ability compared to the conventional integral sliding mode control approach.
This work presents a disturbance observer-based predictive control strategy using a subspace matrix structure. The aim is to improve the capability of classical predictive controllers in handling external disturbances...
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ISBN:
(数字)9781728159539
ISBN:
(纸本)9781728159546
This work presents a disturbance observer-based predictive control strategy using a subspace matrix structure. The aim is to improve the capability of classical predictive controllers in handling external disturbances. A subspace-based predictive controller is designed directly from measurements. Then, a disturbance observer is designed using subspace matrices to estimate the external disturbance. Both of the designs are integrated using a feed-forward plus feed-back strategy to form the proposed control strategy. The proposed scheme is tested with a simulated model of a pressurized water nuclear reactor. The effectiveness of the proposed technique is demonstrated for two different load-following operations. Further, a quantitative analysis is performed to analyse the control performance of the proposed approach.
This work combines the subspace predictive control technique with the integral sliding mode control strategy to formulate a novel robust subspace predictive control scheme. The subspace predictive controller provides ...
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ISBN:
(数字)9781728159539
ISBN:
(纸本)9781728159546
This work combines the subspace predictive control technique with the integral sliding mode control strategy to formulate a novel robust subspace predictive control scheme. The subspace predictive controller provides the nominal control whereas the integral sliding mode controller gives the discontinuous control action. The aim is to improve the capability of subspace predictive controller in handling uncertainties and external disturbances. The proposed control scheme is evaluated with a simulated pressurized water nuclear reactor. The effectiveness of the proposed technique is demonstrated for two different load-following operations in the presence of uncertainties.
In the nuclear power plants (NPPs), fault detection and diagnosis (FDD) methods are very important to improve the safety and reliability of plants. Researchers have established various FDD methods such as model-based ...
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ISBN:
(数字)9781728192109
ISBN:
(纸本)9781728192116
In the nuclear power plants (NPPs), fault detection and diagnosis (FDD) methods are very important to improve the safety and reliability of plants. Researchers have established various FDD methods such as model-based methods, data-driven methods, and signal-based methods. In practical applications, model-based methods are very difficult to achieve. Thus, various data-driven methods and signal- based methods have been applied for monitoring key subsystems in NPPs. In this paper, a brief overview of the Artificial Neural Network (ANN) based FDD method is presented. Simulated data have been generated to train the ANNs as per requirement and to compare with the plant signal during a fault. A technique has been proposed analyzing two sensors data (power sensor and coolant sensor) to determine the sensor and actuator fault in a closed-loop in presence of robust (Proportional-Integral-Derivative) PID controller. Results are produced with credible MATLAB simulation.
This paper proposes a state feedback output tracking Model Reference Adaptive control (MRAC) to control the power of a nuclear reactor. A linearized version of nuclear reactor dynamics based on point kinetics modeling...
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This paper proposes a state feedback output tracking Model Reference Adaptive control (MRAC) to control the power of a nuclear reactor. A linearized version of nuclear reactor dynamics based on point kinetics modeling, with parameter uncertainties, is utilized for the control studies. Normalized negative gradient algorithm is used to update the controller gains adaptively. Stability of the closed loop system is established through Lyapunov analysis and Barbalet’s lemma. Performance of controller is validated through non linear simulation studies.
This paper formulates a methodology of on-line subspace identification in wavelet-based multiresolution framework. The proposed strategy integrates proficiency of wavelets for multiscale data representation with the r...
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