Radial basis function neural network controller for power control of molten salt breeder reactor of nuclear power plant

Naha, Sulakshan; Das, Dushmanta Kumar

doi:10.1016/j.anucene.2023.110160

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…RBFs are specific type of functions that deliver an output depending on the absolute difference between the input and a central parameter. RBFs have been successfully utilized as activation functions in neural networks for modeling and control of highly nonlinear systems such as biomass boilers [49], unmanned aerial vehicles [50] and new generation nuclear power plants [51].…”

Section: Hybrid Modelmentioning

confidence: 99%

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Aguilar,

Chanal,

Chamagne

et al. 2024

Energies

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The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.

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Section: Hybrid Modelmentioning

confidence: 99%

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Aguilar,

Chanal,

Chamagne

et al. 2024

Energies

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Robust controller design strategies with improved performance for MSBR core

Dwivedi,

Ali,

Chanya

2025

Nuclear Engineering and Design

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Observer-Based Adaptive Prescribed- Time H _∞ Coordinated Control for Multiple Robot Manipulators With Prescribed Performance and Input Quantization

Li,

Liu,

Tian

2024

IEEE Access

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In this paper, a prescribed-time adaptive H ∞ cooperative strategy with novel prescribed performance and input quantization based on a leader state observer is proposed for uncertain multiple robot manipulators. First, a novel prescribed performance function is introduced into the asymmetric log-type barrier Lyapunov function, which can limit angle errors and reduce the loss of communication resources among multiple robot manipulators. Second, a new prescribed-time leader state estimation observer is proposed to estimate the leader's state information and pass it to the other followers without acceleration information. Third, a prescribed-time adaptive command filter is designed to solve the ''explosion of the complexity'' problem and improves the convergence performance of the control system. Fourth, an adaptive prescribed-time H ∞ cooperative controller with novel prescribed performance and input quantization is designed. Finally, simulation examples are presented to evaluate the stability of the proposed control system.INDEX TERMS Prescribed-time H ∞ control, novel prescribed performance, asymmetric log-type barrier Lyapunov function, prescribed-time leader state estimation observer, prescribed-time adaptive command filter, multiple robot manipulators.

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Radial basis function neural network controller for power control of molten salt breeder reactor of nuclear power plant

Cited by 3 publications

References 14 publications

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Robust controller design strategies with improved performance for MSBR core

Observer-Based Adaptive Prescribed- Time H _∞ Coordinated Control for Multiple Robot Manipulators With Prescribed Performance and Input Quantization

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Radial basis function neural network controller for power control of molten salt breeder reactor of nuclear power plant

Cited by 3 publications

References 14 publications

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Robust controller design strategies with improved performance for MSBR core

Observer-Based Adaptive Prescribed- Time H ∞ Coordinated Control for Multiple Robot Manipulators With Prescribed Performance and Input Quantization

Contact Info

Product

Resources

About

Observer-Based Adaptive Prescribed- Time H _∞ Coordinated Control for Multiple Robot Manipulators With Prescribed Performance and Input Quantization