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Computationally Efficient Learning-Based Controller for Bidirectional Interleaved DC-DC Converter
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DOI：10.1109/JESTIE.2025.3526649.png)
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Abstract
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This article proposes a model-free control strategy for the nonisolated bidirectional interleaved dc-dc converter, which is frequently used to regulate the battery charging and discharging processes. The proposed controller simultaneously offers fast dynamic response, very low computational complexity, and closed-loop control stability by employing the machine learning techniques. A systematic control design guideline is provided. The efficacy of the proposed controller is verified by experimental results.
Keywords:
Mathematical models
Batteries
Stability criteria
Robustness
Process control
Voltage control
PI control
H infinity control
Computational complexity
Tuning
Battery
bidirectional interleaved DC-DC converter
neural network
nonlinear control
stability
Journal
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138
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