Perancangan Pengaturan Kecepatan Pada Motor Arus Searah Tanpa Sikat Menggunakan Neural Network Berbasis Particle Swarm Optimization.

Prabowo, Irwan Eko (2016) Perancangan Pengaturan Kecepatan Pada Motor Arus Searah Tanpa Sikat Menggunakan Neural Network Berbasis Particle Swarm Optimization. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

[img]
Preview
Text
2211100085-Undergraduate-Thesis.pdf - Published Version

Download (1MB) | Preview

Abstract

Brushless Direct Current Motor ( Motor BLDC ) merupakan pengembangan dari teknologi motor DC yang telah ada. Motor ini dapat berfungsi tanpa menggunakan sikat pada komutatornya. Sikat / brush yang ada, merupakan salah satu kelemahan dari motor DC dikarenakan perlunya perawatan pada sikat secara berkala. Motor BLDC sendiri memiliki berbagai keunggulan dibandingkan dengan motor DC konvensional. Pada tugas akhir ini, saya merancang sebuah sistem motor BLDC, kemudian digunakan kontroler Neural Network memaksimalkan efektifitas kerja motor pada segala kondisi. Pada Neural Network sendiri memiliki kemampuan untuk memperbaiki kinerja sistemnya melalui proses learning dengan melakukan perubahan pada nilai bobotnya. Pada proses learning, untuk optimisasi perubahan nilai bobot maka dapat digunakan Particle Swarm Optimization ( PSO ). Dari Hasil Learning yang didapat diperoleh parameter nilai bobot w1=0,136782366779, w2= 0,56795706196, w3= 0,00235166854, dan w4=1 ================================================================================================================== Brushless Direct Current Motor (BLDC Motor) is a development of the DC motor technology. This motor can function without the use of brushes in its comutator. Brush that exist, is one of the disadvantages of DC motor due to the need for care in the brush on a regular basis. BLDC motor itself has various advantages compared to the conventional DC motors. In this final task, I designed a system of BLDC motor controller, then used Neural Network to maximize the effectiveness of the motor work in all conditions. On Neural Network alone has the ability to improve the performance of the system through the process of learning by doing it, the weight on the value of Neural Network change. On the process of learning, for optimization changes the value of weight then it can be used a Reinforced learning using Particle Swarm Optimization (PSO) Algorithm.From the Learning process we get the parameter of weight w1=0.13678236677, w2= 0.56795706196, w3= 0.00235166854, and w4=1.

Item Type: Thesis (Undergraduate)
Additional Information: RSE 621.46 Pra p
Uncontrolled Keywords: Motor arus searah tanpa sikat, Brushless DC, PSO-NN , Neural Network
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL521.3 Automatic Control
Divisions: Faculty of Industrial Technology > Electrical Engineering > (S1) Undergraduate Theses
Depositing User: Users 13 not found.
Date Deposited: 10 Jul 2017 06:24
Last Modified: 27 Dec 2018 04:14
URI: http://repository.its.ac.id/id/eprint/41954

Actions (login required)

View Item View Item