Rancang Bangun Sistem Kontrol Kecepatan Motor BLDC Menggunakan ANFIS Untuk Aplikasi Sepeda Motor Listrik

Mohamad, Ridwan (2017) Rancang Bangun Sistem Kontrol Kecepatan Motor BLDC Menggunakan ANFIS Untuk Aplikasi Sepeda Motor Listrik. Masters thesis, Institut Teknologi Sepuluh Nopember.

[thumbnail of 07111550010202-Master_Thesis.pdf]
Preview
Text
07111550010202-Master_Thesis.pdf - Accepted Version

Download (3MB) | Preview

Abstract

Sistem kontrol kecepatan motor BLDC menggunakan ANFIS telah didesain dan diimplementasikan. Algoritma ANFIS mampu mengkontrol kecepatan motor BLDC sesuai dengan nilai refrensi yang diinginkan. Rata-rata error steady state yang dicapai dengan menggunakan ANFIS adalah sebesar 0,1 % dengan rise time sebesar 2,7437 s untuk kecepatan referensi sebesar 4000 rpm. Proses pembelajaran ANFIS menggunakan metode hybrid PSO dan RLSE dengan supervisi dari Fuzzy-PID. PSO dan RLSE dapat mentraining data ANFIS multi output dengan sangat baik. Data training terbaik dicapai saat nilai λ=1 dengan error RMSE sebesar 0,05364. Waktu eksekusi algoritma ANFIS pada mikrokontroler adalah sebesar 96 us. ======================================================================================================
BLDC motor speed control system using ANFIS has been designed and
implemented. ANFIS algorithm is able to control the speed of the BLDC motor
according to the desired reference value. The average of steady state error
achieved using ANFIS is 0.1% and the rise time is 2.7437 s when the reference
speed is 4000 rpm. ANFIS learning process uses hybrid PSO and RLSE methods
supervised by Fuzzy-PID. PSO and RLSE can train the ANFIS multi-output data
very well. The best training data is achieved when the value of λ = 1 with RMSE
error of 0.05364. The execution time of ANFIS algorithm on microcontroller is 96
us.

Item Type: Thesis (Masters)
Additional Information: RTE 629.89 Rid r-1 3100018074302
Uncontrolled Keywords: motor BLDC; sistem kontrol; fuzzy logic; PID; ANFIS; PSO; RLSE; kendaraan listrik; BLDC motor; control system; fuzzy logic; electric vehicle
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3070 Automatic control
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7872 Electric current converters, Electric inverters.
T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL220 Electric vehicles and their batteries, etc.
Divisions: Faculty of Electrical Technology > Electrical Engineering > 20101-(S2) Master Thesis
Depositing User: Mohamad Ridwan
Date Deposited: 06 Feb 2018 08:33
Last Modified: 02 May 2020 00:20
URI: http://repository.its.ac.id/id/eprint/49393

Actions (login required)

View Item View Item