Pambudi, Fauzi Agung (2019) Pengaturan Tekanan Pada Rig 38-714 Menggunakan Metode Kontroler PI Berbasis Neural-Fuzzy Hibrida Adaptif. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sistem pengaturan proses memiliki beberapa variabel yang dapat diatur, salah satunya adalah tekanan udara. Pengaturan tekanan udara berfungsi untuk menjaga titik kerja dalam keadaan stabil, akan tetapi pengendalian variabel ini memiliki permasalahan yang sering terjadi yaitu adanya perubahan beban. Untuk mengatasi permasalahan pengendalian tersebut, pada tugas akhir ini dilakukan penelitian mengenai perancangan kontroler PI berbasis neural-fuzzy. Dari hasil implementasi kontroler pada plant Rig 38-714 didapatkan hasil respon implementasi dengan overshoot < 5 % dan nilai error steady state mendekati 0 dan untuk permasalahan perubahan beban, kontroler dapat beradaptasi dengan perubahan tekanan dan memenuhi spesifikasi perubahan tekanan yang diinginkan, tidak melebihi 50 % dari set point serta error steady state mendekati 0.
========================================================================== The process management system has several variables that can be regulated, one of which is air pressure. Air pressure regulation functions to keep the working point stable, but controlling this variable has a problem that often occurs, namely the change in load. To overcome these control problems, in this final project a research is conducted on the design of neural-fuzzy PI controllers. From the results of the implementation of the controller on the Rig 38-714 plant the results of the implementation response with <5% overshoot and steady state error values approached 0 and for load change problems, the controller can adapt to pressure changes and meet the desired pressure change specifications not exceeding 50% of set point and steady state error close to 0.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSE 629.89 Pam p-1 2019 |
| Uncontrolled Keywords: | PI control, neural-fuzzy, Rig 38-714 |
| Subjects: | Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) Q Science > QA Mathematics > QA9.64 Fuzzy logic Q Science > QA Mathematics > QA248_Fuzzy Sets T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105.585 TCP/IP (Computer network protocol) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7871.674 Detectors. Sensors |
| Divisions: | Faculty of Electrical Technology > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Fauzi Agung Pambudi |
| Date Deposited: | 16 Mar 2023 04:03 |
| Last Modified: | 16 Mar 2023 04:03 |
| URI: | http://repository.its.ac.id/id/eprint/63720 |
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