| 引用本文: | 杨瑞康,葛高飞,张作轩,赵军波,马辉.基于RBF神经网络的单相三电平APF终端滑模控制[J].控制理论与应用,2026,43(1):61~68.[点击复制] |
| YANG Rui-kang,GE Gao-fei,ZHANG Zuo-xuan,ZHAO Jun-bo,MA Hui.Terminal sliding mode control based on RBF neural network for single-phase three-level APF[J].Control Theory & Applications,2026,43(1):61~68.[点击复制] |
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| 基于RBF神经网络的单相三电平APF终端滑模控制 |
| Terminal sliding mode control based on RBF neural network for single-phase three-level APF |
| 摘要点击 209 全文点击 40 投稿时间:2025-04-17 修订日期:2025-12-23 |
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| DOI编号 10.7641/CTA.2025.50166 |
| 2026,43(1):61-68 |
| 中文关键词 有源电力滤波器 滑模控制 RBF神经网络 三电平变换器 |
| 英文关键词 active power filter sliding mode control RBF neural network three-level converter |
| 基金项目 国家自然科学基金项目(52377191),湖北省自然科学基金项目(2024AFB584)资助. |
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| 中文摘要 |
| 传统电流电压双闭环策略中,滑模控制器对于系统模型参数具有较强的依赖性,导致有源电力滤波器的电
流内环控制器存在鲁棒性下降、动态响应迟缓等问题.为此,本文提出一种基于径向基函数(RBF)神经网络的双闭
环滑模控制策略,以提高补偿电流动态响应速度和鲁棒性.该控制策略内环采用RBF神经网络全局快速终端滑模控
制器;外环采用线性滑模控制器.RBF神经网络通过在线逼近未知项以降低对模型的依赖性,全局快速终端滑模控
制器用于提高系统收敛性.实验结果表明,所提控制策略能够使单相三电平有源电力滤波器在稳态和动态工况下,
均展现出更优越的电流跟踪性能与更强的鲁棒性. |
| 英文摘要 |
| In the traditional current-voltage double closed-loop strategy, the sliding mode controller has a strong depen
dence on the system model parameters, which leads to problems such as reduced robustness and sluggish dynamic response
in the current inner loop controller of the active power filter. To address this, this paper proposes a double closed-loop
sliding mode control strategy based on radial basis function (RBF) neural networks to improve the dynamic response speed
and robustness of the compensation current. The inner loop of this control strategy adopts a RBF neural network global
fast terminal sliding mode controller, while the outer loop uses a linear sliding mode controller. The RBF neural network
reduces the dependence on the model by online approximation of unknown terms, and the global fast terminal sliding
mode controller is used to enhance the system’s convergence. Experimental results show that the proposed control strat
egy enables the single-phase three-level active power filter to exhibit superior current tracking performance and stronger
robustness under both steady-state and dynamic operating conditions. |
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