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

Original Article
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Abstract

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.

Keywords

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

Notes

Acknowledgements

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.

References

  1. 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
  2. 2.
    Narongrit T, Areerak K, Areerak K (2015) A new design approach of fuzzy controller for shunt active power filter. Electr Power Compon Syst 43(6):685–694CrossRefGoogle Scholar
  3. 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
  4. 4.
    Ma K, Fei J (2015) Model reference adaptive fuzzy control of a shunt active power filter. J Intell Fuzzy Syst 28(1):485–494MathSciNetGoogle Scholar
  5. 5.
    Hou S, Fei J (2015) Adaptive fuzzy backstepping control of three-phase active power filter. Control Eng Pract 45:12–21CrossRefGoogle Scholar
  6. 6.
    Li MM, Verma B (2016) Nonlinear curve fitting to stopping power data using rbf neural networks. Expert Syst Appl 45(C):161–171CrossRefGoogle Scholar
  7. 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
  8. 8.
    Cuong PV, Wang YN (2016) Adaptive trajectory tracking neural network control with robust compensator for robot manipulators. Neural Comput Appl 27(2):525–536CrossRefGoogle Scholar
  9. 9.
    Tabatabaei SM, Arefi MM (2016) Adaptive neural control for a class of uncertain non-affine nonlinear switched systems. Nonlinear Dyn 83(3):1773–1781MathSciNetCrossRefMATHGoogle Scholar
  10. 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. 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. 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
  13. 13.
    Xu L, Qian F, Li Y, Li Q, Yang YW, Xu J (2015) Resource allocation based on quantum particle swarm optimization and rbf neural network for overlay cognitive ofdm system. Neurocomputing 173:1250–1256CrossRefGoogle Scholar
  14. 14.
    Sun F, Xiong R, He H (2014) A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique. Appl Energy 162:1399–1409CrossRefGoogle Scholar
  15. 15.
    Czarnowski I, Jędrzejowicz P (2016) Agent-based rbf network classifier with feature selection in a kernel space. Cybern Syst 47(1–2):17–31CrossRefGoogle Scholar
  16. 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
  17. 17.
    Isikveren A (2016) Aircraft ice accretion prediction using neural network and wavelet packet transform. Aircr Eng Aerosp Technol Int J 88(1):128–136CrossRefGoogle Scholar
  18. 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. 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. 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. 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
  22. 22.
    Lin FJ, Shen PH (2006) Adaptive fuzzy-neural-network control for a dsp-based permanent magnet linear synchronous motor servo drive.IEEE Trans on Fuzzy Syst 14(4):481–495CrossRefGoogle Scholar
  23. 23.
    El-Sousy FFM (2014) Adaptive hybrid control system using a recurrent rbfn-based self-evolving fuzzy-neural-network for PMSM servo drives. Appl Soft Comput 21(8):509–532CrossRefGoogle Scholar
  24. 24.
    Lin FJ, Tan KH, Fang DY (2014) Squirrel-cage induction generator system using hybrid wavelet fuzzy neural network control for wind power applications. Neural Comput Appl 26(4):911–928CrossRefGoogle Scholar
  25. 25.
    Fei J, Yan W (2014) Adaptive global fast terminal sliding mode control of mems gyroscope using fuzzy-neural-network. Nonlinear Dyn 78(1):103–116CrossRefMATHGoogle Scholar
  26. 26.
    Hsu C, Lin C, Li M (2011) Adaptive dynamic RBF fuzzy neural controller design with a constructive learning. Int J Fuzzy Syst 13(3):175–184MathSciNetGoogle Scholar
  27. 27.
    Taormina R (2015) Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and extreme learning machines. J Hydrol 529(3):1617–1632CrossRefGoogle Scholar
  28. 28.
    Zhang J (2009) Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization. J Univ Comput Sci 15(4):840–858MathSciNetGoogle Scholar
  29. 29.
    Wang W (2015) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour Manag 29(8):2655–2675CrossRefGoogle Scholar
  30. 30.
    Li Y, Tong S (2015) Adaptive fuzzy output feedback dynamic surface control of interconnected nonlinear pure-feedback systems. IEEE Trans on Cybern 45(1):138–149CrossRefGoogle Scholar
  31. 31.
    Li Y, Tong S (2015) Prescribed performance adaptive fuzzy output-feedback dynamic surface control for nonlinear large-scale systems with time delays. Inf Sci 292:125–142MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Xu B, Sun F (2017) Composite intelligent learning control of strict-feedback systems with disturbance. IEEE Trans Cybern.  https://doi.org/10.1109/TCYB.2017.2655053 Google Scholar
  33. 33.
    Xu B, Yang D, Shi Z, Pan Y, Chen B, Sun F (2017) Online recorded data based composite neural control of strict-feedback systems with application to hypersonic flight dynamics. IEEE Trans Neural Netw Learning Syst.  https://doi.org/10.1109/TNNLS.2017.2743784 Google Scholar
  34. 34.
    Xu B, Wang D, Zhang Y, Shi Z (2017) DOB based neural control of flexible hypersonic flight vehicle considering wind effects. IEEE Trans Ind Electron 64(11):8676–8685CrossRefGoogle Scholar
  35. 35.
    Wang X, Dong C, Fan T (2007) Training T-S norm neural networks to refine weights for fuzzy if-then rules. Neurocomputing 70(13–15):2581–2587.  https://doi.org/10.1016/j.neucom.2007.01.005 CrossRefGoogle Scholar
  36. 36.
    Wang X, Li C(2005) A new definition of sensitivity for RBFNN and its applications to feature reduction. Lecture Notes Comput Sci 3496:81–86CrossRefMATHGoogle Scholar
  37. 37.
    Wang X, Zhang T, Wang R (2017) Non-iterative deep learning: incorporating restricted Boltzmann machine into multilayer random weight neural networks. IEEE Trans Syst Man Cybern Syst.  https://doi.org/10.1109/TSMC.2017.2701419 Google Scholar

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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