基于RBF神经网络的单相三电平APF终端滑模控制OA

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.