Identification of Industrial Furnace Temperature for Sintering Process in Nuclear Fuel Fabrication Using NARX Neural Networks

Sutarya, Dede; Kusumoputro, Benyamin

doi:10.1155/2014/854569

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The neural network is arranged using a Nonlinear AutoRegressive with eXogenous (NARX) input model structure, by feeding back the outputs to the input of the network. In nuclear area, NARX input model for identification of industrial furnace temperature of sintering process has been investigated effectively [18]. The model of NARX input can be derived from …”

Section: Particle Swarm Optimization-based Direct Inverse Controlmentioning

confidence: 99%

Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor

Pambudi

Wahab²,

Kusumoputro³

2016

Science and Technology of Nuclear Installations

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A neural network-direct inverse control (NN-DIC) has been simulated to automatically control the power level of nuclear reactors. This method has been tested on an Indonesian pool type multipurpose reactor, namely, Reaktor Serba Guna-GA Siwabessy (RSG-GAS). The result confirmed that this method still cannot minimize errors and shorten the learning process time. A new method is therefore needed which will improve the performance of the DIC. The objective of this study is to develop a particle swarm optimization-based direct inverse control (PSO-DIC) to overcome the weaknesses of the NN-DIC. In the proposed PSO-DIC, the PSO algorithm is integrated into the DIC technique to train the weights of the DIC controller. This integration is able to accelerate the learning process. To improve the performance of the system identification, a backpropagation (BP) algorithm is introduced into the PSO algorithm. To show the feasibility and effectiveness of this proposed PSO-DIC technique, a case study on power level control of RSG-GAS is performed. The simulation results confirm that the PSO-DIC has better performance than NN-DIC. The new developed PSO-DIC has smaller steady-state error and less overshoot and oscillation.

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Section: Particle Swarm Optimization-based Direct Inverse Controlmentioning

confidence: 99%

Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor

Pambudi

Wahab²,

Kusumoputro³

2016

Science and Technology of Nuclear Installations

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“…The inversion technique initiates the direct inverse control (DIC) method with the use of ANN. The ANN method has been applied for nonlinear systems from chemical reactor [21] to nuclear purposes [22], whereas, in the field of unmanned system's controller, the DIC-ANN becomes research topics in UAV, for example, hexacopter [19], helicopter [23], and quadrotor [24] to the AUV [8].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

Muliadi

Kusumoputro

2018

Journal of Advanced Transportation

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This article proposes a comparative method to assess the performance of artificial neural network's direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

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Preparation of low-temperature sintered UO2 nanomaterials by radiolytic reduction of ammonium uranyl tricarbonate

Wang

Chen

Shen

2016

Journal of Nuclear Materials

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Identification of Industrial Furnace Temperature for Sintering Process in Nuclear Fuel Fabrication Using NARX Neural Networks

Cited by 5 publications

References 16 publications

Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor

Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

Preparation of low-temperature sintered UO2 nanomaterials by radiolytic reduction of ammonium uranyl tricarbonate

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