有源电力滤波器的自适应RBFNN模糊滑模控制

Tengteng Wang, J. Fei
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引用次数: 1

摘要

为了提高并联型有源电力滤波器的性能,提出了一种自适应径向基函数(RBF)神经网络模糊控制方案。将RBF神经网络用于APF动态模型中非线性函数的逼近,根据李雅普诺夫稳定性分析的自适应规律在线调整RBF神经网络的权值,以保证状态撞击滑动面并沿滑动面滑动。为了补偿网络逼近误差和消除原有的抖振,采用自适应模糊系统对滑模控制项进行调整,提高了系统的鲁棒性。采用该方法对有源滤波器进行仿真,验证了所提控制器的有效性,显示出良好的补偿性能和较强的鲁棒性。
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Adaptive RBFNN fuzzy sliding mode control for active power filter
This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance performance of shunt active power filter (APF). The RBF NN is utilized on the approximation of nonlinear function in APF dynamic model, the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis, to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method confirm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.
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