控制理论与应用2026，Vol.43Issue(1)：61-68,8.DOI:10.7641/CTA.2025.50166
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
Abstract
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.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Key words
active power filter/sliding mode control/RBF neural network/three-level converter引用本文复制引用
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):61-68,8.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.Supported by the National Natural Science Foundation of China(52377191)and the Natural Science Fund of Hubei Province(2024AFB584). （52377191）
