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

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.

scholarly journals Development of Two Wheeled Robot (TWR) by Single Stepper Driver Using PID Controller

2020 ◽  
Author(s):  
Vasvani Ashish Maheshbhai ◽  
DEEPAK KUMAR ◽  
Ravi Sinha
Keyword(s):  
Pid Controller ◽  
Mechanical Stability ◽  
Inertial Mass ◽  
Vertical Position ◽  
Mass Unit ◽  
Complementary Filter ◽  
Wheeled Robot ◽  
Stepper Motors ◽  
Essential Region ◽  
Balancing Robot

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.

Fuzzy Logic Based Path Planning for Industrial Robot

2020 ◽  
pp. 355-364
Author(s):  
Supriya Sahu ◽  
Bibhuti Bhusan Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Control System ◽  
Path Planning ◽  
Industrial Robot ◽  
Industrial Robots ◽  
The Other ◽  
Membership Functions ◽  
Fuzzy Logic Systems ◽  
Pick And Place

This article describes how industrial robots are generally used to perform different tasks in industries, such as pick and place, and many more operations in industries. Among these, pick and place is a very common and frequently used task. Path planning is the most important thing in order to make any process more economical. The main focus of the research is to design a fuzzy control system for path planning for industrial robots using artificial intelligence using fuzzy logic. For the analysis, ten different tasks are tested. For fuzzy logic systems, three membership functions are analyzed and compared to find the best result. From the research, it has been found that a Gaussian membership function gives more accurate result in comparison to the other two membership functions.

Pengaruh Jumlah Input Dan Membership Function Fuzzy Logic Control Pada Robot Keseimbangan Beroda Dua

2019 ◽  
Vol 8 (02) ◽  
pp. 19-24
Author(s):  
Indra Dwisaputra ◽  
Tareg Mahmoud ◽  
Meti Megayanti ◽  
Irvan Budiawan ◽  
Pranoto Hidaya R
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Model ◽  
Tilt Angle ◽  
Fuzzy Logic Control ◽  
Membership Functions ◽  
Plant Model ◽  
Logic Control ◽  
Output Torque ◽  
First Case ◽  
Balancing Robot

In this paper, a dynamic model of two-wheeled balancing robot has been created, and the two types of FLC has been designed. The Mamdani methods used on both FLC. The first FLC uses pendulum tilt angle theta (θ) as the input and it requires the motor torque to keep the robot remains balanced as the output. The second FLC uses two inputs, the first input is theta (θ) and the second input is the change in the value of theta (θ) which is the output torque of the motor. The second plant model and FLC built by using Matlab Simulink. The first case is one input using 5 membership functions (mf). The second case is two inputs using the 5 and 7 mf. The characteristics and effects of the changes in the input and mf have been simulated in the Simulink and compared. By expanding the number of the inputs can reduce motor specification required in balancing robot. Meanwhile, by increasing the number mf, it can improve the performance of the controller much faster to reach the settling time.

scholarly journals SINGULAR AXIS SELF BALANCING ROBOT

2013 ◽  
pp. 258-260
Author(s):  
SULABH KUMRA ◽  
SHILPA MEHTA
Keyword(s):  
Control System ◽  
Pid Control ◽  
Balance Model ◽  
Balance System ◽  
Balancing Robot ◽  
The Stability ◽  
The Right ◽  
Limited Accuracy

In this paper, we presented the Balance model as a singular axis self balancing robot that is capable of adjusting itself with respect to changes in weight and position. We developed the Balance System from a single servo and a single accelerometer. The stability of the system is to show the capabilities of the ATMega8 in doing PID loops even with limited accuracy in position readings. PID control system is designed to monitor the motors so as to keep the system in equilibrium. It should be easily reproducible given the right parts and code.

scholarly journals Design of Fuzzy Membership Functions for Predicting Student’s Knowledge Performance

2019 ◽  
Vol 3 (6) ◽  
Author(s):  
Salisu Muhammad Sani
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Problem Solving ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Membership ◽  
Linguistic Variables ◽  
Membership Functions ◽  
Fuzzy Membership Functions

A Fuzzy logic controller is a problem-solving control system that provides means for representing approximate knowledge. The output of a fuzzy controller is derived from the fuzzifications of crisp (numerical) inputs using associated membership functions. The crisp inputs are usually converted to the different members of the associated linguistic variables based on their respective values. This point is evident enough to show that the output of a fuzzy logic controller is heavily dependent on its memberships of the different membership functions, which can be considered as a range of inputs [4]. Input membership functions can take various forms trapezoids, triangles, bell curves, singleton or any other shape that accurately enables the distribution of information within the system, in as much as the shape provides a region of transition between adjacent membership functions.

scholarly journals Fuzzy Logic Control System Stability Analysis Based on Lyapunov’s Direct Method

2009 ◽  
Vol 4 (4) ◽  
pp. 415 ◽  
Author(s):  
Radu-Emil Precup ◽  
Marius-Lucian Tomescu ◽  
Ștefan Preitl
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Stability Analysis ◽  
Control Systems ◽  
Fuzzy Logic Control ◽  
Direct Method ◽  
Nonlinear Processes ◽  
Logic Control ◽  
Lyapunov’S Direct Method ◽  
The Stability

A stability analysis method for nonlinear processes controlled by Takagi- Sugeno (T-S) fuzzy logic controllers (FLCs) is proposed. The stability analysis of these fuzzy logic control systems is done in terms of Lyapunov’s direct method. The stability theorem presented here ensures sufficient conditions for the stability of the fuzzy logic control systems. The theorem enables the formulation of a new stability analysis algorithm that offers sufficient stability conditions for nonlinear processes controlled by a class of T-S FLCs. In addition, the paper includes an illustrative example that describes one application of this algorithm in the design of a stable fuzzy logic control system.

scholarly journals Synthesis of Multicriteria Controller by Means of Fuzzy Logic Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Andrew Lozynskyy ◽  
Lyubomyr Demkiv
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Control System ◽  
Membership Functions ◽  
Displacement Control ◽  
System A ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Optimal Values ◽  
Linear And Nonlinear

A two-mass fuzzy control system is considered. For fuzzification process, classical both linear and nonlinear membership functions are used. To find optimal values of membership function’s parameters, genetic algorithm is used. To take into account values of both output and intermediate parameters of the system, a penalty function is considered. Research is conducted for the case of speed control system and displacement control system. Obtained results are compared with the case of the system with classical, crisp controller.

Sign in / Sign up

Export Citation Format

Share Document