Adaptive fuzzy-neural-network based on RBFNN control for active power filter

Original Article


In this paper, an adaptive fuzzy-neural-network (FNN) control scheme based on a radial basis function (RBF) neural network (NN) is proposed to enhance the performance of a shunt active power filter (APF). APF can efficiently eliminate harmonic contamination and improve the power factor compared with traditional passive filter. The proposed approach gives a RBF NN control scheme, which is utilized on the approximation of a nonlinear function in APF dynamic model, the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis. In addition, adaptive fuzzy-neural-network systems is employed to compensate the neural approximation error and eliminate the existing chattering, enhancing the robust performance of the system. Simulation results confirm the effectiveness of the proposed controller, demonstrating that APF with the proposed method has strong robustness and the outstanding compensation performance.


Radial basis function (RBF) Fuzzy-neural-network control (FNN) Adaptive control Active power filter 



The authors thank the anonymous reviewers for their useful comments that improved the quality of the paper. This work is partially supported by National Science Foundation of China under Grant No. 61374100; Natural Science Foundation of Jiangsu Province under Grant No. BK20171198, the Fundamental Research Funds for the Central Universities under Grant No. 2017B20014, 2017B21214.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of IoT EngineeringHohai UniversityChangzhouChina
  2. 2.Jiangsu Key Lab. of Power Transmission and Distribution Equipment TechnologyChangzhouChina

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