Munir, Syahrul (2018) Perancangan Jaringan Syaraf Tiruan Sebagai Estimator Load Disturbance Torque Pada Sistem Servo Modular MS150 DC. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Penggunaan motor DC telah digunakan secara luas baik pada sistem yang membutuhkan sensitivitas kecil hingga sensitivitas yang tinggi. Pada sistem dengan sensitivitas yang tinggi butuh untuk menjaga kepresisian semaksimal mungkin bahkan akibat adanya load disturbance torque pada shaft motor yang terhubung pada rotor. Dikarenakan memiliki tingkat kesulitan yang tinggi untuk melakukan pengukuran secara langsung, estimasi digunakan untuk mengukur besarnya load disturbance torque pada shaft motor. Untuk mengatasi permasalaan ini, dilakukan penelitian mengenai perancangan estimasi load disturbance torque pada motor DC dengan menggunakan jaringan syaraf tiruan (JST). Dari penelitian ini, telah dirancang model Jaringan Syaraf Tiruan (JST) untuk estimasi load disturbance torque dengan jumlah node terbaik sebanyak 3 node masukan, antara lain kecepatan sudut, tegangan masukan, dan arus, 9 hidden node, dan 1 node keluaran, yakni load disturbance torque. Dari hasil pelatihan JST didapatkan nRMSE sebesar 3,75025% dan dari hasil pengujian didapatkan nRMSE sebesar 4,03775%. Sehingga dapat disimpulkan bahwa JST telah mampu mengestimasi load disturbance torque dengan nRMSE dibawah 7%.=======================================================The use of DC motors has been widely used both on systems that require low sensitivity up to high sensitivity. In systems with high sensitivity, it is necessary to maintain the maximum possible precision even due to the load disturbance torque on the motor shaft connected to the rotor. Due to having a high difficulty for direct measurement, estimates are used to measure the amount of load disturbance torque on the motor shaft. To overcome this problem, a study conducted for load disturbancetorque estimation on DC motor using artificial neural network (ANN). From this research, Artificial Neural Network (ANN) model has been designed to estimate the load disturbance torque with the best node quantity of 3 input nodes, including angular velocity, input voltage, and current, 9 hidden nodes and 1 output node, ie load disturbance torque. From the results of training ANN obtained nRMSE of 3.75025% and from the test results obtained nRMSE of 4.03775%. So it can be concluded that ANN has been able to estimate the load disturbance torque with nRMSE below 7%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | motor DC, load disturbance torque, jaringan syaraf tiruan, Levenberg-Marquardt, RMSE |
| Subjects: | Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2785 Electric motors, Induction. |
| Divisions: | Faculty of Industrial Technology > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Syahrul Munir |
| Date Deposited: | 21 Jun 2021 11:01 |
| Last Modified: | 21 Jun 2021 11:01 |
| URI: | http://repository.its.ac.id/id/eprint/54504 |
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