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ITS » Master Theses » Sistem Pengaturan S2
Posted by dewi007 at 08/05/2009 10:13:51  •  6484 Views


PERANCANGAN PENGENDALI FUZZY ADAPTIF DENGAN MENGGUNAKAN NEURAL NETWORK PADA SISTEM PRESSURE CONTROL TRAINER FEEDBACK 38-714

DESIGN OF ADAPATIVE FUZZY CONTROLLER TUNED BY NEURAL NETWORK FOR PRESSURE CONTROL TRAINER FEEDBACK 38-714

Author :
Sultoni, Arif Indro 




ABSTRAK

Aplikasi pengaturan tekanan pneumatik dipengaruhi oleh berbagai macam obyek termanipulasi sehingga banyak parameter yang berubah selama berlangsungnya proses. Diperlukan kontroler adaptif untuk mengatasi permasalahan tersebut. Pada penelitian ini akan didesain sebuah pengendali fuzzy adaptif dengan menggunakan neural network. Neural network akan melakukan pembelajaran secara berkesinambungan dan selanjutnya mengubah parameter kontroler fuzzy sesuai dengan perubahan yang terjadi pada plant. Struktur kontroler ini dirancang untuk mengendalikan sebuah plant pneumatik dengan nama Pressure Control Trainer Feedback 38-714 yaitu suatu peralatan yang melingkupi karakteristik proses tekanan dengan komponen yang sesuai dengan standar industri. Hasil simulasi menunjukkan respon yang dihasilkan mempunyai settling time 500 detik maximum overshoot 22 dan MSE 0728. Performa ini lebih baik dibandingkan dengan kontroler fuzzy non-adaptif yang mempunyai nilai settling time 750 detik maximum overshoot 222 dan MSE 1489 maupun kontroler PI dengan nilai settling time 800 detik maximum overshoot 444 dan MSE 0912.


ABSTRACT

An improved self-tuning mechanism of fuzzy controller by neural network will be presented. The membership function parameter is tuned by neural network. The control structure will be designed and simulated for pressure control trainer Feedback 38-714 prototype of a pneumatic control system which complied on industrial standard. Adaptive fuzzy controller is being used to handle widely parameter changing of the plant such that non-adaptive control can not be. Settling time 500 second maximum overshoot 2.2 and MSE 0.728 are performance of the adaptive controller response. These performances are better than non-adaptive controller which have settling time 750 second maximum overshoot 22.2 and MSE 1.489 or PI controller with settling time 800 second maximum overshoot 44.4 and MSE 0.912.



Keywordsfuzzy adaptif; neural network; sistem tekanan pneumatik
 
Subject:  sistem kontrol biologis
Contributor
  1. Ir. Katjuk Astrowulan, MSEE.
    Ir. Ali Fatoni, M.T.
Date Create: 08/05/2009
Type: Text
Format: pdf.
Language: Indonesian
Identifier: ITS-Master-3100009034305
Collection ID: 3100009034305
Call Number: RTE 629.836 Sul p


Source
Master Theses of Electrical Engineering, RTE 629.836 Sul p, 2008

Coverage
ITS Community Only

Rights
Copyright @2008 by ITS Library. This publication is protected by copyright and permission should be obtained from the ITS Library prior to any prohibited reproduction, storage in a retrievel system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permission(s), write to ITS Library




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ITS-Master-3100009034305-3982.pdf




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