Implementasi Kendali Keseimbangan Gerak Two Wheels Self Balancing Robot Menggunakan Fuzzy Logic

Self balancing robot is a two-wheeled robot that only has two fulcrums so that this robot is an unbalanced system. Therefore, a control system that can maintain the stability of the robot is needed so that the robot can keep in standing position. This study aims to design a self-balancing robot and its control system which improves the robot's performance against the maximum angle of disturbance that can be overcome. The control system used is based on fuzzy logic with 9 membership functions and 81 rules. The control system is applied to the ESP-32 microcontroller with the MPU-6050 sensor as a feedback position of the robot and DC motor as an actuator. Complementary filters are added to the MPU-6050 sensor readings to reduce noise to obtain better robotic tilt angle readings. The improvement of this research compared to previous research based on fuzzy is the addition of the number of membership functions from 7 to 9 and the embedding of a complementary filter on the MPU-6050 sensor output reading. The result shows that the designed self balancing robot which has dimensions of 10cm x 18cm x 14.5cm can cope with the maximum disturbance angle up to 17.5⁰.

Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter

In robot inertial navigation systems, to deal with the problems of drift and noise in the gyroscope and accelerometer and the high computational cost when using extended Kalman filter (EKF) and particle filter (PF), a complementary filtering algorithm is utilized. By combining the Inertial Measurement Unit (IMU) multi-sensor signals, the attitude data are corrected, and the high-precision attitude angles are obtained. In this paper, the quaternion algorithm is used to describe the attitude motion, and the process of attitude estimation is analyzed in detail. Moreover, the models of the sensor and system are given. Ultimately, the attitude angles are estimated by using the quaternion extended Kalman filter, linear complementary filter, and Mahony complementary filter, respectively. The experimental results show that the Mahony complementary filtering algorithm has less computational cost than the extended Kalman filtering algorithm, while the attitude estimation accuracy of these two algorithms is similar, which reveals that Mahony complementary filtering is more suitable for low-cost embedded systems.

Sistem Keseimbangan Robot ERISA Pada Bidang Miring Menggunakan Kontrol PD dan Sensor Fusion

The world of robotics is currently developing; many robots are created to help humans work in carrying out daily activities. Various types of robots have been created, one of which is a type of robot that resembles a human body (humanoid). Humanoid robots are developing in many countries, including Indonesia. In its development, the walking technique is a major factor in making humanoid robots. Humanoid robots have the ability to walk like humans by balancing their body positions while walking, so they don’t fall. In maintaining this balance, a tilt detection system for the robot’s position and a balancing system is needed when the robot is about to fall. So, to overcome this problem, implementation is carried out using the sensor fusion method at the output of the sensors that are used to minimize noise or interference with the sensor output value. The accuracy of the sensor output value on the position angle tilt detector can help the robot provide a balancing act. By implementing a PD control system and sensor fusion consisting of a Kalman filter and a complementary filter, the robot was successfully carried up and down the plane to a maximum slope of 12 °.

A Two Wheel Self-Balancing Vehicle

This paper deals with the development of the equation of motion and practical implementation of low cost two-wheel self-balancing model of a Segway transporter. The experimental model of cart was designed and made under this study. Nonlinear equations of motion of real model and linearized model were derived. To develop the mathematical model, Matlab/Simulink was applied. The mechanical part was implemented into Simulink, and a DC motor was considered as a linear system. The real model was tested for its balance by implementation of a control algorithm consisting of a complementary filter and PID algorithm on an Arduino development board with peripheral devices. The fully functional self-balancing model was used as a demonstration in the teaching process of the Mechatronics courses.

Torque Measurement Technology by Using a Magnetostrictive Ring and Multiple Magnets

A torque measurement method that combines the inverse magnetostrictive effect with twist angle measurement has been proposed in this paper. A Kalman filter and complementary filter are applied to significantly reduce the noises and errors in the proposed method. This new measurement block can detect the applied torque with an error of less than 1%. The proposed measurement method can be used in static and dynamic conditions.