IPAC2018 - List of Authors (Lee, E.J.)

Paper	Title	Page
WEPAL030	Deep Learning Based Predictive Control for RFT-30 Cyclotron	2230
	Y.B. Kong, M.G. Hur, E.J. Lee, J.H. Park, H.S. Song, S.D. Yang KAERI, Daejon, Republic of Korea
	Successful construction of the control system is an important problem in the accelerator. The RFT-30 cyclotron is 30 MeV cyclotron for radioisotope production and fundamental researches. To operate the RFT-30 cyclotron for beam irradiation, the human operators should carefully manipulate the control parameters. If the control does not function properly, it becomes difficult to handle the cyclotron and cannot perform the accurate operations for the control. In this work, we propose a deep learning based model predictive control approach for the RFT-30 cyclotron. The proposed approach is composed of two steps: system identification and a control design. In the system identification procedure, the proposed approach constructs the predictive model of the accelerator using the deep learning approach. In the control design stage, the controller finds the optimal control inputs by solving the optimization problem. To analyze the performance of the proposed approach, we applied the approach into the RFT-30 cyclotron.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAL030
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Paper

Title

Page

Deep Learning Based Predictive Control for RFT-30 Cyclotron

2230

Y.B. Kong, M.G. Hur, E.J. Lee, J.H. Park, H.S. Song, S.D. Yang
KAERI, Daejon, Republic of Korea

Successful construction of the control system is an important problem in the accelerator. The RFT-30 cyclotron is 30 MeV cyclotron for radioisotope production and fundamental researches. To operate the RFT-30 cyclotron for beam irradiation, the human operators should carefully manipulate the control parameters. If the control does not function properly, it becomes difficult to handle the cyclotron and cannot perform the accurate operations for the control. In this work, we propose a deep learning based model predictive control approach for the RFT-30 cyclotron. The proposed approach is composed of two steps: system identification and a control design. In the system identification procedure, the proposed approach constructs the predictive model of the accelerator using the deep learning approach. In the control design stage, the controller finds the optimal control inputs by solving the optimization problem. To analyze the performance of the proposed approach, we applied the approach into the RFT-30 cyclotron.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAL030

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)