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• W4386076823 abstract "Finite-control-set model predictive control (FCS-MPC) shows great control performance and adaptability for different converter topologies and operating modes. However. The computation burden increases significantly for long prediction step and multi-level topology. Artificial neuron network (ANN) is developed to imitate FCS-MPC controller for similar control effect with lower computation burden. However, the imitation accuracy is not good enough for single ANN. To achieve acceptable control effect using a simple ANN, we propose an FCS-MPC-based dual-module ANN controller. We first off-line train the ANN to imitating the FCS-MPC. Then we designed a dual-module structure which combines ANN and FCS-MPC to increase the imitation accuracy. The simulation result shows that the accuracy of our design increases to 99.87% while the computation burden is reduced by 58.8% compared with FCS-MPC. It can achieve similarly control performance and significantly reduce computation burden." @default.
• W4386076823 created "2023-08-23" @default.
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• W4386076823 date "2023-05-22" @default.
• W4386076823 modified "2023-09-27" @default.
• W4386076823 title "FCS-MPC Based Dual-module ANN Controller for Three-level Converter" @default.
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• W4386076823 doi "https://doi.org/10.23919/icpe2023-ecceasia54778.2023.10213864" @default.
• W4386076823 hasPublicationYear "2023" @default.
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