ELM-Based Adaptive Practical Fixed-Time Voltage Regulation in Wireless Power Transfer System

This paper proposes an extreme learning machine (ELM)-based adaptive sliding mode control strategy for the receiver-side buck converter system in the wireless power transfer system subjecting to the lumped uncertainty. The proposed control strategy utilizes a singularity-free fixed-time sliding mode...

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Bibliographic Details
Published in:Energies (Basel) 2023-01, Vol.16 (3), p.1016
Main Authors: Hu, Youhao, Zhang, Bowang, Hu, Weikang, Han, Wei
Format: Article
Language:English
Subjects:
Approximation
buck converter
Buck converters
Closed loop systems
Closed loops
Control theory
Design and construction
Electric current converters
Electric current regulators
Electric potential
extreme learning machine
Feedback control
fixed time sliding mode
Fuzzy logic
Machine learning
Neural networks
Sliding mode control
Stability analysis
Transmitters
Uncertainty
Voltage
Voltage regulators
wireless power transfer
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Summary:This paper proposes an extreme learning machine (ELM)-based adaptive sliding mode control strategy for the receiver-side buck converter system in the wireless power transfer system subjecting to the lumped uncertainty. The proposed control strategy utilizes a singularity-free fixed-time sliding mode (FTSM) feedback control, which ensures a fixed-time convergence for both the sliding variable and voltage tracking error. An ELM-based uncertainty bound estimator is further designed to learn the uncertainty bound information in real-time, which opportunely loosens the constraint of bound information requirement for sliding mode control design. The global stability of the closed-loop system is rigidly analyzed, and the good performance of the proposed control strategy is validated by comparison experiments which exhibit ideal overshoot elimination, 45.70–51.72% reduction of settling time, and 13.65–36.96% reduction of the root mean square value for voltage tracking error with respect to different load types.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16031016