Self-scaling reinforcement learning for fuzzy logic controller-applications to motion control of two-link brachiation robot

1999 ◽  
Vol 46 (6) ◽  
pp. 1123-1131 ◽  
Author(s):  
Y. Hasegawa ◽  
T. Fukuda ◽  
K. Shimojima
Keyword(s):  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Motion Control ◽  
Fuzzy Logic Controller

scholarly journals Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1254 ◽  
Author(s):  
Cheng-Hung Chen ◽  
Shiou-Yun Jeng ◽  
Cheng-Jian Lin
Keyword(s):  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Search Algorithm ◽  
Experimental Results ◽  
Average Error ◽  
Stopover Site ◽  
Accumulated Reward ◽  
Wall Following

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

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.

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.

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.

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