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
1995 ◽  
Vol 7 (1) ◽  
pp. 12-20
Author(s):  
Jun Tang ◽  
◽  
Keigo Watanabe ◽  
Masatoshi Nakamura ◽  
◽  
...  

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.


2000 ◽  
Vol 33 (27) ◽  
pp. 135-140
Author(s):  
Arbnor Pajaziti ◽  
Ahmet Shala ◽  
Agron Pajaziti ◽  
Ramë Likaj

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg

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.


2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Dongyeop Kang ◽  
Sangmoon Lee

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