Multiple Neural Control Strategies Using a Neuro-Fuzzy Classifier


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The paper deals with the control of complex dynamic systems. The main objective is to partition the whole operational system domain in local regions using an incremental neuro-fuzzy classifier in order to achieve multiple neural control strategies for the considered system. In our case, this approach is applied to a greenhouse operating during one day. Therefore, banks of neural controllers and direct neural local models are made from different partitioned greenhouse behaviors and two multiple neural control strategies are proposed to control the greenhouse. The selection of the suitable controller is accomplished by computing the minimal output error between desired and direct neural local models outputs in the case of the first control strategy and from a supervisor block containing the considered neuro-fuzzy classifier in the case of the second control strategy. Simulation results are then carried out to show the efficiency of the two control strategies.

About the authors

Khaled Dehmani

Laboratory Lab-STA

Email: Fethi.Fourati@ipeis.rnu.tn
Tunisia, Sfax

Fathi Fourati

Control and Energy Management Laboratory (CEM-Lab)

Author for correspondence.
Email: Fethi.Fourati@ipeis.rnu.tn
Tunisia, Sfax

Khaled Elleuch

Laboratory Lab-STA

Email: Fethi.Fourati@ipeis.rnu.tn
Tunisia, Sfax

Ahmed Toumi

Laboratory Lab-STA

Email: Fethi.Fourati@ipeis.rnu.tn
Tunisia, Sfax

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2018 Allerton Press, Inc.