DC-DC降压变换器的自适应神经反步终端滑模控制
Adaptive Neural Backstepping Terminal Sliding Mode Control of a DC-DC Buck Converter.
作者信息
Gong Xiaoyu, Fei Juntao
机构信息
Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, College of Information Science and Engineering, Hohai University, Changzhou 213022, China.
College of Artificial Intelligence and Automation, Hohai University, Changzhou 213022, China.
出版信息
Sensors (Basel). 2023 Aug 27;23(17):7450. doi: 10.3390/s23177450.
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.
本文针对DC-DC降压变换器，提出了一种基于双隐层递归神经网络（DHLRNN）的自适应反步终端滑模控制（ABTSMC）方法。利用DHLRNN对系统不确定性进行逼近和补偿。在反步控制的基础上，引入终端滑模控制（TSMC）以确保跟踪误差的有限时间收敛。在不同测试条件下的变换器原型上验证了复合控制方法的有效性。实验对比结果表明，所提出的控制方法具有更好的稳态性能和更快的瞬态响应。
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