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.

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.

Implementation of Fuzzy Logic Controller for Wheelchair Motion Control Based on EOG Data

2014 ◽  
Vol 661 ◽  
pp. 183-189
Author(s):  
Nurul Muthmainnah Mohd Noor ◽  
Salmiah Ahmad
Keyword(s):  
Fuzzy Logic ◽  
Eye Movement ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Linear Motion ◽  
Communication Tool ◽  
Forward Motion ◽  
Movement Data ◽  
Logic Control

The study of this paper is to implementation the fuzzy logic control designed for wheelchair motion based on the eye movement signals using electrooculograhphy (EOG) technique. This technique is to acquire the eye movement data from a person, for example, tetraplegia. The tetraplegia is paralysis caused by illness or injury to a human that result in the partial or total loss of use of all their limbs and torso. The eye movement data which was obtained can be used as a main communication tool between human and machine. The PD-type fuzzy controller was successfully designed and tested on the wheelchair model, for control the linear motion (focused for forward motion). The wheelchair model was developed using MSC.Visual Nastran 4D. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set by the EOG signal.

Genetic Q-Fuzzy Based Intelligent Control for Mobile Robot Navigation

2004 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Devendra P. Garg
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Learning Algorithm ◽  
Robot Navigation ◽  
Path Tracking ◽  
Mobile Robot Navigation ◽  
Reinforcement Learning Algorithm

This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is then compared with the one tuned with uncontrolled Genetic Algorithm. The theory is applied to a two-wheeled mobile robot’s path tracking problem. It is shown that the performance of the Fuzzy-Logic controller tuned by Genetic Algorithm controlled via reinforcement learning is better than the performance of the Fuzzy-Logic controller tuned via uncontrolled Genetic Algorithm.

scholarly journals An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 132 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
DoHyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Environmental Temperature ◽  
Fuzzy Inference ◽  
Control Method ◽  
Learning Algorithm ◽  
Membership Functions ◽  
Inference Method ◽  
Logic Control

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

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