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A Model-Free Neuro-Fuzzy Predictive Controller for Compensation of Nonlinear Plant Inertia and Time Delay
Volume 28, Issue 4 (2017), pp. 749–766
Pub. online: 1 January 2017      Type: Research Article      Open accessOpen Access
Received
1 December 2016
Accepted
1 May 2017
Published
1 January 2017

Abstract

The aim is to develop simple for industrial use neuro-fuzzy (NF) predictive controllers (NFPCs) that improve the system performance and stability compensating the nonlinear plant inertia and time delay. A NF plant predictor is trained from real time plant control data and validated to supply a main model-free fuzzy logic controller with predicted plant information. A proper prediction horizon is determined via simulation investigations. The NFPC closed loop system stability is validated based on a parallel distributed compensation (PDC) approximation of the NFPC. The PDC can easily be embedded in industrial controllers. The proposed approach is applied for the real time air temperature control in a laboratory dryer. The improvements are reduced overshoot and settling time.

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Biographies

Yordanova Snejana
sty@tu-sofia.bg

S. Yordanova is a full-time professor of intelligent control systems at the Faculty of Automation of the Technical University of Sofia. She has received her MEng and PhD from the same university. Her research interests are related to the application of robust, fuzzy logic, neural networks and genetic algorithms approaches to modelling, control and prediction of processes in different industries – ecology, power engineering, soda production, etc. She has authored and co-authored one monograph, one chapter in a Springer book, a number of textbooks and manuals and over 130 research papers.

Ichtev Alexandar
ichtev@tu-sofia.bg

A. Ichtev is an associate professor in control theory at the Faculty of Automation of the Technical University of Sofia. He received his MEng and PhD degrees from the same university. His scientific interests are in the field of classical control systems, fault detection and isolation, fault tolerant control, fuzzy control, adaptive control and robust control. He has authored and co-authored one chapter in a book, a number of textbooks and laboratory manuals and over 60 research papers.


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© 2017 Vilnius University
Open access article under the CC BY license.

Keywords
fuzzy logic control genetic algorithms Lyapunov stability fuzzy logic plant predictor real time temperature control

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