| 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, Sta 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
|
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
2. Azizi A., Ali A.Y.B., Ping L.W. (2012)Model development and comparative study of bayesian and ANFIS inferences for uncertain variables of production line in tile industry, WSEAS Transactions on Systems, 11 (1), pp. 22 – 37. - 2012 - в издания, индексирани в Scopus или Web of Science
Вид: статия в списание, публикация в издание с импакт фактор
