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A New Robust Integral Reinforcement Learning Based Control Algorithm for Interleaved DC/DC Boost Converter
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DOI：10.1109/TIE.2022.3179558.png)
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摘要
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This article proposes a novel online integral reinforcement learning (IRL) based data-driven control algorithm for interleaved dc/dc boost converter. The proposed algorithm is independent of system model due to the usage of a three-layer neural network (NN). Furthermore, its controller gains are autonomously adjusted online through the value function based NN weights updating mechanism, which simplifies the controller gain tuning process. Compared to the conventional model-dependent control approaches, it provides superior control performance. Additionally, the proposed method contributes to significantly reduce the computational burden of classical IRL algorithm by removing the disturbance updating process. Experimental results are presented to verify the efficacy of the proposed algorithm.
Keyword:
DC/DC boost
integral reinforcement learning (IRL)
interleaved converter
optimal control
parameter robustness
期刊
IF:
7.2
论文数:
1.8W
被引数:
9.8W
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