Transient Current Constraint of DAB Converter Based on Model Predictive Control

Xiao, Zhongxiu; Liu, Wanjun; Gao, Guoqing; Cui, Yao; Kang, Qiao; Wang, Meng

doi:10.1109/ipemc-ecceasia48364.2020.9367975

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…In the above applications, modeling and designing controllers with specific steady-state and dynamic performance for DAB converters is crucial, various topology variants, modeling and control schemes, and their applications of DAB converters have been proposed in references (Xiao et al, 2020;Ma et al, 2021;Shao et al, 2022a).…”

Section: Introduction 1motivation and Challengesmentioning

confidence: 99%

A robust voltage control of dual active full bridge converter based on RBF neural network sliding mode control with reduced order modeling approach

Wang

Sun

et al. 2023

Front. Energy Res.

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This paper presents a control method of dual active full bridge (DAB) DC/DC converter based on neural network Sliding mode control under the reduced order modeling method, which is used to enhance the output voltage regulation and improve the system robustness. Sliding mode control (SMC) is famous for its robustness and improving the dynamic performance of nonlinear systems. However, it includes drawbacks such as complex modeling, chattering, and decreased tracking performance. Therefore, through the method of reduced order modeling, the Radial Basis Function neural network algorithm is selected to modify and design the traditional sliding mode variable structure controller, completing the design of neural network approximation terms and adaptive laws. The proposed reduced order modeling and RBF(Radial Basis Function Neural Network)-SMC scheme simplifies the dual active full bridge modeling process, completes the parameter approximation of the sliding mode controller, and eliminates the chattering problem. Firstly, through simulation and comparative analysis of commonly used modeling methods for dual active full bridge, a reduced order modeling method was adopted to simplify the design process. Then, by ingeniously designing the sliding mode surface and Radial Basis Function control law, and using neural network to modify the Sliding mode control technology, the chattering problem, load disturbance and voltage fluctuation influence of the sliding mode controller are improved. The stability of the control method is proved by Lyapunov stability theory. Finally, the proposed RBF-SMC method is compared with PI linear control and classical Sliding mode control methods through simulation and experiments to verify the effectiveness of the proposed control method.

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Section: Introduction 1motivation and Challengesmentioning

confidence: 99%