SIMULATION OF AN AIRCRAFT MOTION CONTROL CHANNEL WITH A FUZZY LOGIC CONTROLLER

2020 ◽  
Author(s):  
V. A. Nenashev ◽  
◽  
A. A. Sentsov ◽  
S. A. Ivanov ◽  
◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Motion Control ◽  
Fuzzy Logic Controller ◽  
Control Channel ◽  
Aircraft Motion

Implementation of Wheelchair Motion Control Based on Electrooculography Using Simulation and Experimental Performance Testing

2014 ◽  
Vol 554 ◽  
pp. 551-555
Author(s):  
Nurul Muthmainnah Mohd Noor ◽  
Salmiah Ahmad ◽  
Sharul Naim Sidek
Keyword(s):  
Fuzzy Logic ◽  
Eye Movement ◽  
Motion Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Performance Testing ◽  
Linear Motion ◽  
Communication Tool ◽  
Experimental Performance ◽  
Movement Data

The aim of this study is to perform the experimental verification on the fuzzy-based control designed for wheelchair motion. This motion control based on the eye movement signals using electrooculograhphy (EOG) technique. The EOG is a technique to acquire the eye movement data from a person, i.e tetraplegia, which the data obtained, can be used as a main communication tool. This study is about the implementation of the designed controller using PD-type fuzzy controller and tested on the hardware of the wheelchair system using the eye movement signal obtained through EOG technique as the motion input references. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set (forward and backward direction) by the EOG signal.

FUZZY-BASED CLASSIFIER DESIGN FOR DETERMINING THE EYE MOVEMENT DATA AS AN INPUT REFERENCE IN WHEELCHAIR MOTION CONTROL

2015 ◽  
Vol 76 (8) ◽  
Author(s):  
Nurul Muthmainnah Mohd Noor ◽  
Salmiah Ahmad
Keyword(s):  
Fuzzy Logic ◽  
Eye Movement ◽  
Motion Control ◽  
Fuzzy Logic Controller ◽  
Learning Algorithm ◽  
Numerical Data ◽  
Fuzzy Classification ◽  
Classification Problems ◽  
Good Tool ◽  
Movement Data

Fuzzy logic is widely used in many complex and nonlinear systems for control, system identification and pattern recognition problems. The fuzzy logic controller provides an alternative to the PID controller which is a good tool for control of systems that are difficult to model. In this paper, the fuzzy-based classifiers were designed in order to determine the eye movement data. These data were used as an input reference in wheelchair motion control. Then, a set of an appropriate fuzzy classification (FC) was designed based on the numerical data from eye movement data acquisitions that obtained from the electrooculogram (EOG) technique. Each fuzzy rule (FR) for this system is based on the form of IF-THEN rule. Since membership functions (MFs) are generated automatically, the proposed fuzzy learning algorithm can be viewed as a knowledge acquisition tool for classification problems. The experimental results on eye movement data were presented to demonstrate the contribution of the proposed approach for generating MFs using MATLAB simulink for linear motion in forward direction.

scholarly journals A PID-Type Fuzzy Logic Controller-Based Approach for Motion Control Applications

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5323 ◽  
Author(s):  
José R. García-Martínez ◽  
Edson E. Cruz-Miguel ◽  
Roberto V. Carrillo-Serrano ◽  
Fortino Mendoza-Mondragón ◽  
Manuel Toledano-Ayala ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Motion Control ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Industrial Applications ◽  
Settling Time ◽  
Self Tuning ◽  
S Curve ◽  
Tuning Strategy

Motion control is widely used in industrial applications since machinery, robots, conveyor bands use smooth movements in order to reach a desired position decreasing the steady error and energy consumption. In this paper, a new Proportional-Integral-Derivative (PID) -type fuzzy logic controller (FLC) tuning strategy that is based on direct fuzzy relations is proposed in order to compute the PID constants. The motion control algorithm is composed by PID-type FLC and S-curve velocity profile, which is developed in C/C++ programming language; therefore, a license is not required to reproduce the code among embedded systems. The self-tuning controller is carried out online, it depends on error and change in error to adapt according to the system variations. The experimental results were obtained in a linear platform integrated by a direct current (DC) motor connected to an encoder to measure the position. The shaft of the motor is connected to an endless screw; a cart is placed on the screw to control its position. The rise time, overshoot, and settling time values measured in the experimentation are 0.124 s, 8.985% and 0.248 s, respectively. These results presented in part 6 demonstrate the performance of the controller, since the rise time and settling time are improved according to the state of the art. Besides, these parameters are compared with different control architectures reported in the literature. This comparison is made after applying a step input signal to the DC motor.

scholarly journals Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot

2021 ◽  
Vol 11 (13) ◽  
pp. 6023
Author(s):  
Alexandr Štefek ◽  
Van Thuan Pham ◽  
Vaclav Krivanek ◽  
Khac Lam Pham
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Motion Control ◽  
Fuzzy Logic Controller ◽  
Operation Time ◽  
Wheeled Mobile Robot ◽  
Optimization Process ◽  
Differential Drive ◽  
Full Optimization

The energy-efficient motion control of a mobile robot fueled by batteries is an especially important and difficult problem, which needs to be continually addressed in order to prolong the robot’s independent operation time. Thus, in this article, a full optimization process for a fuzzy logic controller (FLC) is proposed. The optimization process employs a genetic algorithm (GA) to minimize the energy consumption of a differential drive wheeled mobile robot (DDWMR) and still ensure its other performances of the motion control. The earlier approaches mainly focused on energy reduction by planning the shortest path whereas this approach aims to optimize the controller for minimizing acceleration of the robot during point-to-point movement and thus minimize the energy consumption. The proposed optimized controller is based on fuzzy logic systems. At first, an FLC has been designed based on the experiment and as well as an experience to navigate the DDWMR to a known destination by following the given path. Next, a full optimization process by using the GA is operated to automatically generate the best parameters of all membership functions for the FLC. To evaluate its effectiveness, a set of other well-known controllers have been implemented in Google Colab® and Jupyter platforms in Python language to compare them with each other. The simulation results have shown that about 110% reduction of the energy consumption was achieved using the proposed method compared to the best of six alternative controllers. Also, this simulation program has been published as an open-source code for all readers who want to continue in the research.

Sign in / Sign up

Export Citation Format

Share Document