Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor
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 stil...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2016/1065790 |
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| author | Yoyok Dwi Setyo Pambudi Wahidin Wahab Benyamin Kusumoputro |
| author_facet | Yoyok Dwi Setyo Pambudi Wahidin Wahab Benyamin Kusumoputro |
| author_sort | Yoyok Dwi Setyo Pambudi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3f81a171cb584825b8e0f7a862ada2b9 |
| institution | Kabale University |
| issn | 1687-6075 1687-6083 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-3f81a171cb584825b8e0f7a862ada2b92025-08-20T03:35:53ZengWileyScience and Technology of Nuclear Installations1687-60751687-60832016-01-01201610.1155/2016/10657901065790Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose ReactorYoyok Dwi Setyo Pambudi0Wahidin Wahab1Benyamin Kusumoputro2Department of Electrical Engineering, University of Indonesia, Kampus Baru UI, Depok 16424, IndonesiaDepartment of Electrical Engineering, University of Indonesia, Kampus Baru UI, Depok 16424, IndonesiaDepartment of Electrical Engineering, University of Indonesia, Kampus Baru UI, Depok 16424, IndonesiaA 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.http://dx.doi.org/10.1155/2016/1065790 |
| spellingShingle | Yoyok Dwi Setyo Pambudi Wahidin Wahab Benyamin Kusumoputro Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor Science and Technology of Nuclear Installations |
| title | Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor |
| title_full | Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor |
| title_fullStr | Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor |
| title_full_unstemmed | Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor |
| title_short | Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor |
| title_sort | particle swarm optimization based direct inverse control for controlling the power level of the indonesian multipurpose reactor |
| url | http://dx.doi.org/10.1155/2016/1065790 |
| work_keys_str_mv | AT yoyokdwisetyopambudi particleswarmoptimizationbaseddirectinversecontrolforcontrollingthepowerleveloftheindonesianmultipurposereactor AT wahidinwahab particleswarmoptimizationbaseddirectinversecontrolforcontrollingthepowerleveloftheindonesianmultipurposereactor AT benyaminkusumoputro particleswarmoptimizationbaseddirectinversecontrolforcontrollingthepowerleveloftheindonesianmultipurposereactor |