IJREE- Volume 1 Issue 2 Paper 5


Author’s Name :  Balamurugan P | Hemasilviavinothini S

Volume 01 Issue 02  Year 2014  ISSN No:  2349-2503  Page no: 19-23



In recent year various control strategies have been applied to power converters. DC-DC converter such as Buck type, Boost type, Buck-Boost type have been widely used in traditional industrial application, e.g., uninterruptible power supply (U.P.S), power system, dc motor drive, telecommunication equipment, etc. In the conventional method, Fuzzy control scheme is designed for the voltage tracking of a DC-DC Boost converter. The main drawback of only used Fuzzy logic control switching frequency will not constant on during load condition. AFNNC could be introduced. However, the computation cost is much higher than conventional system. In the proposed method presents an Adaptive Neuro – Fuzzy Inference System (ANFIS) control scheme is designed for the voltage tracking control, Harmonic reduction & current control. Then, the Total Sliding Mode Control (TSMC) strategies are introduced to developed for enhancing system robustness during the transient period .The output of the AFNNC scheme can be easily supply to the duty cycle of the power switch in the boost converter without strict constrains on control parameters selection in conventional control strategies.


Boost converter, fuzzy neural network (FNN),Lyapunov stability theorem, total sliding-mode control (TSMC), voltage tracking control


  1. R.Feng, R. M. Nelms, and J. Y. Hung, ?Posicast- based digital con-trol of the buck converter, IEEE Trans. Ind. Electron., vol. 53, no. 3,pp. 759–767, Jun. 2006.
  2. M.Karppanen, T. Suntio, and M. Sippola, ?Dynamical characterization of input-voltage-feed forward-controlled buck converter,? IEEE Trans. Ind.Electron., vol. 54, no. 2, pp. 1005–1013, Apr. 2007.
  3. S.Hiti and D. Borojevic, Robust nonlinear control for boost converter,IEEE Trans. Power Electron., vol. 10, no. 6, pp. 651–658, Nov. 1995.
  4. T.T.Song and H. S. Chung, ?Boundary control of boost converters using state-energy plane,IEEE Trans. Power Electron., vol. 23, no. 2, pp. 551– 563, Mar. 2008.
  5. K.Sundareswaran and V. T. Sreedevi, ?Boost converter controller design using queen-bee-assisted GA,? IEEE Trans. Ind. Electron., vol. 56, no. 3, pp. 778–783, Mar. 2009.
  6. K.C. Wu, A comprehensive analysis of current-mode control for DCM buck-boost converters,? IEEE Trans. Ind. Electron., vol. 51, no. 3, pp. 733– 735, Jun. 2004.
  7. W.S. Liu, J. F. Chen, T. J. Liang, and R. L. Lin, Multicascoded sources for a high-efficiency fuel-cell hybrid power system in high-voltage application, IEEE Trans. Power Electron., vol. 26, no. 3, pp. 931–942, Mar. 2011.
  8. W.M. Lin and C. M. Hong, A new Elman neural network-based control algorithm for adjustable-pitch variable-speed wind-energy conversion systems,IEEE Trans. Power Electron., vol. 26, no. 2, pp. 473–481, Feb. 2011.
  9. B.Yang,W.Li,Y.Zhao, and X. He, Design and analysis of a grid connected photovoltaic power system,IEEE Trans. Power Electron., vol. 25, no. 4, pp. 992–1000, Apr. 2010.