Aqila, Marsha Haya (2023) Design and Dynamic Analysis of Inertia Wheel Pendulum with LQR Controller and Kalman Filter. Other thesis, Institut Teknologi Sepuluh Nopember.
![[thumbnail of 02111942000012-Undergraduate_Thesis.pdf]](http://repository.its.ac.id/style/images/fileicons/text.png) |
Text
02111942000012-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2025. Download (6MB) | Request a copy |
Abstract
Industri otomotif merupakan salah satu sektor yang berkembang pesat, contohnya yaitu kendaraan. Salah satu tantangan kritis pada kendaraan yaitu menjaga stabilitas pada saat pergerakaan secara tiba-tiba dan adanya perubahan kondisi jalan. Prinsip yang bisa dilakukan, yaitu dengan menggunakan inertia wheel pendulum. Secara prinsip, Inertia Wheel Pendulum menggunakan sistem kontrol untuk menggerakkan inertia wheel ke posisi tegak. Penelitian ini terdiri dari desain dan analisis dinamik Inertia Wheel Pendulum dengan kontroler LQR dan Kalman Filter. Pada penelitian ini digunakan tiga jenis model inertia wheel dengan momen inersia yang berbeda. Sistem kontrol didesain dengan menurunkan persamaan gerak sistem Inertia wheel Pendulum dengan persamaan lagrange, kemudian hasil penurunan persamaan gerak digunakan untuk mendesain sistem kendali. Hasil penelitian menunjukkan bahwa momen inersia memiliki dampak signifikan pada sistem inertia wheel pendulum. Inertia wheel dengan momen inersia yang lebih kecil dapat menstabilkan pendulum lebih cepat, sementara momen inersia yang lebih besar menghasilkan kecepatan sudut yang lebih tinggi dan stabilisasi yang lebih lambat. Sistem Inertia Wheel Pendulum menggunakan pengendali LQR ketika dibandingkan dengan menggunakan filter Kalman mencapai stabilisasi dalam kurun waktu lambat, tegangan masukan yang lebih tinggi, yang menyebabkan peningkatan kecepatan sudut pendulum, namun memiliki kecepatan sudut motor BLDC yang lebih lambat. Filter Kalman menawarkan stabilisasi yang lebih cepat dan efisien dengan tegangan masukan yang lebih rendah dan respons yang lebih halus melalui estimasi variabel dan pengurangan noise, sehingga menjadi pilihan sistem pengendalian yang terbaik untuk sistem inertia wheel pendulum.
=================================================================================================================================
The automotive industries are one of the sectors that are growing massively, one example of an automotive industry product is a vehicle. Ensuring vehicle stability during abrupt movements and changing road conditions is a critical challenge. One principle employed to maintain stability is using an inertia wheel pendulum. In principle, an inertia wheel pendulum uses a control system to drive the inertia wheel pendulum to the upward position This research uses an Inertia Wheel Pendulum system with three types of inertia wheel models with different moments of inertia. The control system is designed by deriving the equation of motion of the Inertia Wheel Pendulum System with the Lagrange equation. Then the results of deriving the equations of motion are used to design the control system. The result shows that the moment of inertia value significantly affects the inertia wheel pendulum system. Smaller values lead to faster stabilization, while larger values result in higher angular velocities and slower stabilization. The Inertia wheel pendulum system with LQR controller is stabilized in longer duration with small value of the angular velocity of BLDC motor and requires higher input voltage, leading to increased angular velocity of the pendulum. Meanwhile, the Kalman filter offers faster and more efficient stabilization with smaller input voltage and smoother response through variable estimation and noise reduction, making it the suitable control system for the inertia wheel pendulum.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Inertia Wheel Pendulum, Stability, Control System, LQR, Kalman Filter, Stabilitas, Sistem Kontrol, Filter Kalman |
| Subjects: | T Technology > T Technology (General) > T57.62 Simulation T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models. T Technology > TJ Mechanical engineering and machinery > TJ217.2 Robust control T Technology > TJ Mechanical engineering and machinery > TJ230 Machine design T Technology > TJ Mechanical engineering and machinery > TJ541 Flywheels. |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Mechanical Engineering > 21201-(S1) Undergraduate Thesis |
| Depositing User: | Marsha Haya Aqila |
| Date Deposited: | 08 Aug 2023 03:20 |
| Last Modified: | 08 Aug 2023 03:20 |
| URI: | http://repository.its.ac.id/id/eprint/102817 |
Actions (login required)
 |
View Item |