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A genetically trained simplified ANFIS controller to control nonlinear MIMO systems

This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear multi-input multi-output (MIMO) systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instea...

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Main Authors: Lutfy, Omar F., Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce
Format: Conference or Workshop Item
Language:English
Published: IEEE 2011
Online Access:http://psasir.upm.edu.my/id/eprint/47814/1/A%20genetically%20trained%20simplified%20ANFIS%20controller%20to%20control%20nonlinear%20MIMO%20systems.pdf
http://psasir.upm.edu.my/id/eprint/47814/
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spelling my.upm.eprints.478142019-05-29T06:57:40Z http://psasir.upm.edu.my/id/eprint/47814/ A genetically trained simplified ANFIS controller to control nonlinear MIMO systems Lutfy, Omar F. Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear multi-input multi-output (MIMO) systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. As a result, the proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded genetic algorithm (GA) has been utilized to train this MIMO ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Consequently, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. To demonstrate the accuracy and the generalization ability of the proposed controller, two nonlinear MIMO systems have been selected to be controlled by this controller. In addition, this controller robustness to output disturbances has been also evaluated and the results clearly showed the remarkable performance of this MIMO controller. IEEE 2011 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/47814/1/A%20genetically%20trained%20simplified%20ANFIS%20controller%20to%20control%20nonlinear%20MIMO%20systems.pdf Lutfy, Omar F. and Mohd Noor, Samsul Bahari and Marhaban, Mohammad Hamiruce (2011) A genetically trained simplified ANFIS controller to control nonlinear MIMO systems. In: International Conference on Electrical, Control and Computer Engineering (InECCE 2011), 21-22 June 2011, Kuantan, Pahang. (pp. 349-354). 10.1109/INECCE.2011.5953905
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear multi-input multi-output (MIMO) systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. As a result, the proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded genetic algorithm (GA) has been utilized to train this MIMO ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Consequently, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. To demonstrate the accuracy and the generalization ability of the proposed controller, two nonlinear MIMO systems have been selected to be controlled by this controller. In addition, this controller robustness to output disturbances has been also evaluated and the results clearly showed the remarkable performance of this MIMO controller.
format Conference or Workshop Item
author Lutfy, Omar F.
Mohd Noor, Samsul Bahari
Marhaban, Mohammad Hamiruce
spellingShingle Lutfy, Omar F.
Mohd Noor, Samsul Bahari
Marhaban, Mohammad Hamiruce
A genetically trained simplified ANFIS controller to control nonlinear MIMO systems
author_facet Lutfy, Omar F.
Mohd Noor, Samsul Bahari
Marhaban, Mohammad Hamiruce
author_sort Lutfy, Omar F.
title A genetically trained simplified ANFIS controller to control nonlinear MIMO systems
title_short A genetically trained simplified ANFIS controller to control nonlinear MIMO systems
title_full A genetically trained simplified ANFIS controller to control nonlinear MIMO systems
title_fullStr A genetically trained simplified ANFIS controller to control nonlinear MIMO systems
title_full_unstemmed A genetically trained simplified ANFIS controller to control nonlinear MIMO systems
title_sort genetically trained simplified anfis controller to control nonlinear mimo systems
publisher IEEE
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/47814/1/A%20genetically%20trained%20simplified%20ANFIS%20controller%20to%20control%20nonlinear%20MIMO%20systems.pdf
http://psasir.upm.edu.my/id/eprint/47814/
_version_ 1643833990459686912
score 13.211869

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