Adaptive fuzzy-neural-network based on RBFNN control for active power filter
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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.
KeywordsRadial 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.
- 1.Ruanmakok K, Areerak K, Areerak K, Sangtungtong W (2014) The control of shunt active power filter using sliding mode controller. In: International conference on electrical engineering/electronics, computer, telecommunications and information technology, pp 1–5Google Scholar
- 3.Bandal VS, Madurwar PN (2012) Performance analysis of shunt active power filter using sliding mode control strategies. International workshop on variable structure systems, pp 214–219Google Scholar
- 7.Liu F, Fan S (2009) Adaptive RBFNN fuzzy sliding mode control for two link robot manipulator. In: International conference on artificial intelligence and computational intelligence, pp 272–276Google Scholar
- 10.Ak A, Cansever G (2006) Three link robot control with fuzzy sliding mode controller based on RBF neural network. IEEE international symposium on intelligent control, pp 2719–2724Google Scholar
- 11.Chen L, Lu X, Du Z (2014) RBF neural network modeling based on PCA clustering analysis. In: 2014 IEEE international conference on granular computing (GrC), IEEE Computer Society, pp 35–38Google Scholar
- 12.Tao Y, Zheng J, Lin Y (2016) A sliding mode control-based on a rbf neural network for deburring industry robotic systems. Int J Adv Rob Syst 13(8):1–10Google Scholar
- 16.Gao Q, Hou Y, Li K, Sun Z, Wang C, Hou R (2016) Neural network based active disturbance rejection control of a novel electrohydraulic servo system for simultaneously balancing and positioning by isoactuation configuration. Shock Vib 2016(1):1–9Google Scholar
- 18.Xue H, Jiang JG(2006) Fault detection and accommodation for nonlinear systems using fuzzy neural networks. In: Power electronics and motion control conference, 2006. IPEMC 2006. CES/IEEE 5th international 3, pp 1–5Google Scholar
- 19.Lin CH, Wei CY, Wang MT (2011) The fuzzy neural network control with adaptive algorithm for a PM synchronous motor drive. Ind Electron Appl 124:2518–2523Google Scholar
- 20.Wen S, Yan Y (2014) Adaptive fuzzy neural network control for a class of uncertain mimo nonlinear systems via sliding-mode design. Intelligent human-machine systems and cybernetics (IHMSC). In: 2014 sixth international conference on IEEE. pp 2Google Scholar
- 21.Lin CH, Lin CP (2009) Adaptive backstepping FNN control for a permanent magnet synchronous motor drive. Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE conference on IEEE, pp 1293–1298Google Scholar