Development of Self-Balancing Bicycle Using Inertia Wheel: Sensor System

Daniswara, Radya (2024) Development of Self-Balancing Bicycle Using Inertia Wheel: Sensor System. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Self-balancing bicycle is a bicycle that can maintain its balance automatically. Self balancing bicycles generally use balancing control systems and sensors to maintain the balance of the bicycle. The challenges in self-balancing bicycle itself consist of maintaining stability, nonlinear characteristics, parameter variations, and uncontrolled external disturbances. One of the nonlinear characteristics that can occur is noise that comes from sensor measurements. This noise causes errors in sensor readings so that the readings become bad. Based on this, this research will aim to get good sensor readings in the self balancing bicycle prototype. One of strategies to reduce this noise is to use filtering such as Kalman filter. This leads to another objective which is how the implementation of the Kalman filter affects the sensor reading performance of the self-balancing bicycle prototype. The main sensor used in this research is the IMU MPU6050. Furthermore, the digital filtering methods that will be used in this research are lowpass filter and Kalman filter. Sensor reading and filtering will be done using coding on the Arduino ide. In addition, the RMSE equation will be used to calculate and compare the error reduction that occurs. Based on the research results, the error of the raw reading can be reduced by 50.40% when using Kalman filter. This value is obtained when the error in the raw value is 0.27 and in the filter is 0.13. The parameters used in this Kalman filter itself are Q = 0.001 and R = 0.03. Lowpass filter itself can reduce the error by 29.87% of the raw reading. This result occurs when the raw sensor error value is 0.36 and the filter is 0.13. The parameter used in this Lowpass filter itself is the cut off frequency value of 5 Hz. In addition to error reduction, the response of filtering is also seen when applied to the self balancing bicycle prototype. In non-static conditions, the Kalman filter is able to filter noise with a reading response that has a delay of 0.1 s. While the Lowpass filter is able to filter noise with a reading response that has a delay of 6 s.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Self Balancing, Sensor, Kalman filter
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ213 Automatic control. 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:	Panji Radya Daniswara
Date Deposited:	12 Aug 2024 07:39
Last Modified:	12 Aug 2024 07:39
URI:	http://repository.its.ac.id/id/eprint/115342

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