首页|Investigation of data-driven model predictive control for liquid nitrogen cooling on helium refrigerator
Investigation of data-driven model predictive control for liquid nitrogen cooling on helium refrigerator
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NETL
NSTL
Elsevier
The helium refrigerator, which is a critical infrastructure of the fusion device, should be controlled well and maintain stability. During the operation of one refrigerator in the Comprehensive Research Facility for Fusion Technology, a continuous oscillation behavior was observed in the liquid nitrogen (LN2) cooling system. This paper explores a data-driven Model Predictive Control (MPC) scheme for the LN2 cooling control. Modeling the complex system dynamics under the oscillation disturbance is achieved by the encoder-decoder recurrent neural network, which provides an end-to-end implementation for multistep prediction. The data-driven MPC applies the particle swarm optimization algorithm to find the optimal control actions, in which a novelty particle initialization method is adopted to improve the search efficiency. The performance of the data-driven MPC is evaluated by closed-loop simulation, and the simulation results indicate that the disturbance can be effectively restrained. The proposed scheme shows a promising extension prospect, such as smoothing the pulse heat load disturbance in the fusion cryogenic system.
Model predictive controlEncoder-decoderRecurrent neural networkLiquid nitrogen coolingHelium refrigeratorHEATIDENTIFICATIONOPTIMIZATIONDESIGN
Yu, Qiang、Zhou, Zhiwei、Yuan, Kai、Li, Shanshan、Zhu, Zhigang、Zhuang, Ming
NETL
NSTL
Elsevier
