Self balancing robot using complementary filter: Implementation and analysis of complementary filter on SBR

Abstract The mechanical stability is an important parameter for the development of specific robots. Nowadays, it has turned into an essential region of research in the current development, due to increased applications of robots in various fields such as biomedical, aerospace, marines etc. In this paper, Two wheeled Robot (TWR) is designed and fabricated following the concept of an inverted pendulum. It balances itself up in the vertical position. The Proportional Integral Derivative (PID) controller was utilized to locate its stable transformed position. The developed two wheeled robot (TWR) was controlled using angle of pendulum (). It consists of two stepper motors, one arduino Nano microcontroller, Inertial Mass Unit (IMU) sensor and stepper motor driver. An IMU sensor comprises of 3-axis accelerometer and 3-axis gyroscope which measure acceleration and angular velocity. The angle of robot () with respect to the vertical position is computed from the mesaured data. It helps the wheel to prevent from fall by providing the acceleration according to its inclination () from the vertical position. Further, complementary filter was used to compensate gyro drifts with the help of accelerometer readings. PID constants were tuned until optimum values are achieved. Performances were compared with ardunio UNO based Self-Balancing Robot [20] & found that the performance of TWR is almost similar to ardunio UNO based Self-Balancing Robot.

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Balancing Robot Menggunakan Metode Kendali Proporsional Integral Derivatif

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.7157 ◽

2015 ◽

Vol 5 (1) ◽

pp. 89

Author(s):

Rizka Bimarta ◽

Agfianto Eko Putra ◽

Andi Dharmawan

Keyword(s):

Pid Control ◽

Inverted Pendulum ◽

Control Method ◽

Angular Acceleration ◽

Set Point ◽

Tuning Method ◽

Dc Motors ◽

Complementary Filter ◽

Proportional Integral Derivative Control ◽

Balancing Robot

AbstrakPendulum terbalik memiliki pusat gravitasi yang berada diatas poros putar sehingga menyebabkan pendulum terbalik tidak seimbang. Suatu kendali khusus dibutuhkan agar pendulum seimbang dengan cara menggerakkan kereta beroda yang menjadi tumpuan dari pendulum. Penerapan pendulum terbalik dapat ditemui pada balancing robot. Tujuan dari penelitian ini adalah merancang bangun sebuah sistem pengendalian robot dengan dua roda menggunakan sistem kendali untuk membuat robot yang seimbang (balancing robot). Sistem ini mempunyai masukan akselerometer yang digunakan untuk mengukur percepatan sudut (m/s2) dan giroskop untuk mengukur kecepatan sudut (rad/s). Luaran dari akselerometer dan giroskop digabungkan dengan metode complementary filter untuk mendapatkan nilai sudut. Sudut yang diperoleh kemudian dibandingkan dengan set point yang nilainya 0o. Nilai selisih dari set point dan sudut complementary filter diolah menggunakan metode kendali Proporsional Integral Derivatif. Proses kendali PID ini diprogram pada Arduino IDE yang hasilnya diumpankan ke motor DC untuk mengatur kecepatan putar motor DC. Untuk arah putar motor DC ditentukan apabila sudut complementary filter kurang dari nol, maka motor akan berputar mundur. Sedangkan jika sudut complementary filter lebih dari nol, maka motor akan berputar maju. Nilai konstanta PID berdasarkan hasil tuning dengan metode Ziegler-Nichols metode osilasi adalah Kp=1.5, Ki=0.75, Kd=1.85 dan nilai koefisien pada algoritma complementary filter adalah a=0.96. Kata kunci—inverted pendulum, balancing robot, kendali PID, IMU, complementary filter Abstract Center of gravity’s inverted pendulum is located above its pivot point therefore inverted pendulum is unstable. Specific control is needed so that inverted pendulum stable which is by move the cart where the pendulum is mounted. Inverted pendulum application can be found in balancing robot. The purpose of this research is to design a system to control a two wheeled robot using the control system to balance it.The inputs are accelerometer to measure angular acceleration (m/s2) and gyroskop to measure angular velocity (rad/s). The output’s of accelerometer and gyroscope are fused by complementary filter algorithm method to get the actual angle. The actual angle is then compared to set point which is 0o. The differences between set point and actual angle are processed using Proportional Integral Derivative control method. The process of PID control is programmed using Arduino IDE which its result is fed to DC motors. The direction of DC motors are determined by two conditions, if actual angle less than zero then DC motors will spin backwards. Whereas if actual angle more than zero then DC motors will spin forward. The PID control’s constans value based on Ziegler-Nichols Oscillation tuning method are Kp=1.5, Ki=0.75, Kd=1.875 and complementary filter’s coefficient is a=0.96. Keywords— inverted pendulum, balancing robot, PID control, IMU, complementary filter

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

Jurnal Teknik Elektro ◽

10.15294/jte.v13i2.33414 ◽

2021 ◽

Vol 13 (2) ◽

pp. 89-97

Author(s):

Khoirudin Fathoni ◽

Ababil Panji Pratama ◽

Nur Azis Salim ◽

Vera Noviana Sulistyawan

Keyword(s):

Fuzzy Logic ◽

Control System ◽

Standing Position ◽

Sensor Output ◽

Membership Functions ◽

Complementary Filter ◽

Maximum Angle ◽

Wheeled Robot ◽

Balancing Robot ◽

The Stability

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.

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The design of self-balancing robot

Mechanical, Information and Industrial Engineering ◽

10.2495/icmiie140361 ◽

2015 ◽

Author(s):

ZhenXing Wang

Keyword(s):

Balancing Robot

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Design and implementation of control system for two-wheeled self-balancing robot

JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT ◽

10.3724/sp.j.1187.2013.00750 ◽

2014 ◽

Vol 27 (8) ◽

pp. 750-758 ◽

Cited By ~ 1

Author(s):

Wenjian Lin ◽

Hang Zhong ◽

Fuhai Li ◽

Xianghui Xiao ◽

Xinran Qian

Keyword(s):

Control System ◽

Design And Implementation ◽

Balancing Robot

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Flexible Two-wheeled Self-balancing Robot Control Based on Hopfield Neural Network

ROBOT ◽

10.3724/sp.j.1218.2010.00405 ◽

2010 ◽

Vol 32 (3) ◽

pp. 405-413

Author(s):

Xiaogang RUAN ◽

Jianwei ZHAO

Keyword(s):

Neural Network ◽

Robot Control ◽

Hopfield Neural Network ◽

Balancing Robot

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Two-Wheeled Self-Balancing Robot

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) ◽

10.1109/saci51354.2021.9465568 ◽

2021 ◽

Author(s):

Flavius-Catalin Paulescu ◽

Iosif Szeidert ◽

Ioan Filip ◽

Cristian Vasar

Keyword(s):

Balancing Robot

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Displacement Estimation Based on Optical and Inertial Sensor Fusion

Sensors ◽

10.3390/s21041390 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1390

Author(s):

Tomasz Ursel ◽

Michał Olinski

Keyword(s):

Sensor Fusion ◽

Inertial Sensor ◽

External Disturbance ◽

Linear Displacement ◽

Measuring System ◽

Displacement Estimation ◽

Mems Accelerometer ◽

Pedestrian Navigation ◽

Complementary Filter ◽

Optical Flow Sensor

This article aims to develop a system capable of estimating the displacement of a moving object with the usage of a relatively cheap and easy to apply sensors. There is a growing need for such systems, not only for robots, but also, for instance, pedestrian navigation. In this paper, the theory for this idea, including data postprocessing algorithms for a MEMS accelerometer and an optical flow sensor (OFS), as well as the developed complementary filter applied for sensor fusion, are presented. In addition, a vital part of the accelerometer’s algorithm, the zero velocity states detection, is implemented. It is based on analysis of the acceleration’s signal and further application of acceleration symmetrization, greatly improving the obtained displacement. A test stand with a linear guide and motor enabling imposing a specified linear motion is built. The results of both sensors’ testing suggest that the displacement estimated by each of them is highly correct. Fusion of the sensors’ data gives even better outcomes, especially in cases with external disturbance of OFS. The comparative evaluation of estimated linear displacements, in each case related to encoder data, confirms the algorithms’ operation correctness and proves the chosen sensors’ usefulness in the development of a linear displacement measuring system.

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