Autors: Yordanova, S. T., Petrova R.A., Mastorakis N N., Mladenov, V. M.
Title: Sugeno Predictive Neuro-Fuzzy Controller for Control of Nonlinear Plant under Uncertainties
Keywords: Anaerobic organic waste degradation, MATLAB, Prediction, Stability, Sugeno Neuro-Fuzzy Controller

Abstract: The aim of the present investigation is to develop a simple Sugeno neuro-fuzzy predictive controller (NFPC) for the control of a nonlinear plant under uncertainties. The Sugeno NFPC is based on the synergism of a Sugeno PI-type neuro-fuzzy controller (NFC) and a Sugeno plant predictor (PP). The Sugeno NFC is an ANN, trained to simplify and equivalently substitute a two-level Mamdani controller with auto-tuning scaling coefficients. The Sugeno PP is an ANN, trained to predict the next step plant behaviour. Training data in both cases is provided by simulation experimentation on a nonlinear plant subjected to signal and parameter disturbances. The main contributions are: a designed NFPC using ANFIS of MATLAB for the control of the biogas production rate in the anaerobic digestion of organic waste in wastewater treatment; study by simulation of the closed loop system stability and dynamic performance. The system with the Sugeno NFPC has better than thea system with Sugeno NFC.

References

    Issue

    WSEAS Trans. on Systems, vol. 5, issue 8, pp. 1814-1821, 2006, Greece, ISBN ISSN: 1109-2777

    Цитирания (Citation/s):
    1. Pai T.Y., T.J.Wan, S.T. Hsu, T.C. Chang, Y.P. Tsai, C.Y. Lin, H.C. Su, L.F. Yu Using Fuzzy Inference System to Improve Neural Network for Predicting Hospital Wastewater Treatment Plant Effluent, J. Computers and Chemical Engineering 33 (2009), pp. 1272–1278. - 2009 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор