Gunawan, Jeffrey (2018) Kontrol Kecepatan Motor Sepeda Listrik Menggunakan Force Sensor dan Elektromiografi (EMG). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Torque Assisted Bicycle (TAB) merupakan sebuah sepeda dengan penambahan motor listrik sebagai bantuan bagi penggunanya. Sepeda TAB ini dapat mempermudah pengguna dalam berkendara dan ramah lingkungan karena tidak menghasilkan gas emisi. Namun, sepeda TAB masih belum terlalu diminati di Indonesia. Salah satu penyebabnya ialah penggunaan sensor torsi yang mahal sehingga harga sepeda ini menjadi mahal. Pada penelitian ini, sensor tekanan dan sensor EMG digunakan untuk mengganti sensor tersebut. Data dari dua sensor ini diolah dengan menggunakan mikrokontroler dan sistem Artificial Neural Network untuk mengenali data dari dua sensor yang digunakan. Sepeda TAB ini telah dirancang untuk mengenali tingkat aktivitas kaki pengguna sehingga mampu untuk memberikan bantuan kepada pengguna dari motor listrik yang dipasang di sepeda. Daya listrik pada motor ini akan bertambah seiring dengan bertambahnya tekanan pada kaki pengguna. Sinyal dari sensor EMG dan tekanan diolah dengan mikrokontroler Arduino Nano. Pada pengujian statis, subyek mengayuh sepeda pada penyangga selama 10 detik pada 3 tingkat beban yang berbeda dari penggunaan rem. Dari hasil pengujian tersebut, sistem mampu mengenali tiga tingkat beban pada 4 subyek dengan tingkat ketelitian 83,33%. Pada pengujian dinamis, subyek mengayuh sepeda pada 4 tingkat kemiringan permukaan yang berbeda, yakni 0 derajat, 10 derajat, 15 derajat, 20 derakat. Dari hasil pengujian dinamis, sistem mampu mengenali 3 tingkat beban dengan ketelitian rata-rata 75%. Tingkat ketelitian pada dua pengujian tersebut ditinjau dari respon LED yang dihasilkan pada alat. =============== Torque Assisted Bicycle is a bike with an addition of an electrical motor as a support for its user. This bike helps its user to ride comfortably without exerting much force and energy when biking and is environmentally friendly as it does not produce any waste. However, TAB is not well known in Indonesia. One of the reason why is because this bike commonly uses a torque sensor, which is an expensive device. Therefore, a pressure sensor and muscle sensor is used to replace the expensive torque sensor. Data from these two sensors will then be processed by a microcontroller and an Artificial Neural Network system in order to classify the data used. This bike is designed to learn and identify its user’s foot activity and give a response using the electric motor embedded accordingly. The speed of this electric motor will increase along with the rise in its user’s foot activity. Arduino Nano is used to process incoming input from both sensor. Static testing phase is performed when subject performs pedaling motion for 10 seconds on 3 different load which comes from the brake pedal. Results from static testing phase show that system is able to recognize 3 different load on 4 subjects with a 83,33% accuracy. Dynamic testing phase is performed when subject performs pedaling motion in ground with 4 different inclination degree, which are 0 degree, 10 degree, 15 degree, 20 degree. Result from dynamic testing shows that system is able to recognize 3 different load with a 75% average accuracy. Accuracy rate is measured by looking at the response of the LED in this device.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | ANN, EMG, Sensor Tekanan, Sepeda Listrik, TAB, Speed control, microcontroller and an Artificial Neural Network system, Force Sensor, Electric Bike |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5102.9 Signal processing. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7871.674 Detectors. Sensors T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7882.P3 Pattern recognition systems T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL220 Electric vehicles and their batteries, etc. |
| Divisions: | Faculty of Electrical Technology > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Jeffrey Gunawan |
| Date Deposited: | 23 Oct 2018 03:35 |
| Last Modified: | 12 Mar 2021 12:10 |
| URI: | http://repository.its.ac.id/id/eprint/52839 |
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