Control of a mobile robot using generalized dynamic fuzzy neural networks

2004 ◽  
Vol 28 (9) ◽  
pp. 491-498 ◽  
Author(s):  
M.J. Er ◽  
Tien Peng Tan ◽  
Sin Yee Loh
Keyword(s):  
Neural Networks ◽  
Mobile Robot ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural

scholarly journals Block Hierarchical Fuzzy-Neural Networks and Their Application to a Mobile Robot Control

1995 ◽  
Vol 7 (1) ◽  
pp. 12-20
Author(s):  
Jun Tang ◽  
◽  
Keigo Watanabe ◽  
Masatoshi Nakamura ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural Networks ◽  
Small Scale ◽  
Circular Path ◽  
Single Output ◽  
Fuzzy Neural ◽  
Input Variables

If some fuzzy sets in a fuzzy-neural network are assigned to each scalar input data, then the number of intermediate unit functions grows exponentially as the number of input variables to the fuzzy reasoning increases. Therefore, it is very important for multi-input/multi-out-put systems to effectively construct a small-scale fuzzy neural network. In this paper, four types of block hierarchical fuzzy-gaussian neural networks (FGNNs) are proposed for a control system of a mobile robot with two independent driving wheels by applying two inputs and single-output FGNN block, or single-input and singleoutput FGNN block. Such a block hierarchical FGNN consists of three layers. In other words, the first input layer consists of two FGNN blocks that independently generate torques for controlling the velocity and azimuth of the mobile robot. The second hidden layer determines their distributions to the final layer by using fixed connection weights. The final output layer also consists of two FGNN bl ks that automatically determine the out put scalers for the actual left- and right-wheel driving torques. The effectiveness of the proposed method is illustrated through some simulations of a circular path tracking control.

scholarly journals Velocity and Torque Feedback Control of a Nonholonomic Mobile Robot Using Fuzzy-Neural Networks

2000 ◽  
Vol 33 (27) ◽  
pp. 135-140
Author(s):  
Arbnor Pajaziti ◽  
Ahmet Shala ◽  
Agron Pajaziti ◽  
Ramë Likaj
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Mobile Robot ◽  
Fuzzy Neural Networks ◽  
Nonholonomic Mobile Robot ◽  
Fuzzy Neural

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Hotbox level detection of railway vehicle using fuzzy neural networks

2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Railway Vehicle ◽  
Fuzzy Neural

scholarly journals Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Dongyeop Kang ◽  
Sangmoon Lee
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Memory Control
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