Adaptive Neural Backstepping Terminal Sliding Mode Control of a DC-DC Buck Converter

In this paper, an adaptive backstepping terminal sliding mode control (ABTSMC) method based on a double hidden layer recurrent neural network (DHLRNN) is proposed for a DC-DC buck converter. The DHLRNN is utilized to approximate and compensate for the system uncertainty. On the basis of backstepping...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-08, Vol.23 (17), p.7450
Main Authors: Gong, Xiaoyu, Fei, Juntao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Approximation
backstepping control
Buildings and facilities
Controllers
Coordinate transformations
DC-DC buck converter
Design
double hidden layer recurrent neural network
Electric current converters
Electric power systems
Fuzzy logic
Methods
Neural networks
Power electronics
terminal sliding mode control
Wind power
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Summary:In this paper, an adaptive backstepping terminal sliding mode control (ABTSMC) method based on a double hidden layer recurrent neural network (DHLRNN) is proposed for a DC-DC buck converter. The DHLRNN is utilized to approximate and compensate for the system uncertainty. On the basis of backstepping control, a terminal sliding mode control (TSMC) is introduced to ensure the finite-time convergence of the tracking error. The effectiveness of the composite control method is verified on a converter prototype in different test conditions. The experimental comparison results demonstrate the proposed control method has better steady-state performance and faster transient response.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23177450