A model reference control structure using a fuzzy neural network

1995 ◽  
Vol 73 (3) ◽  
pp. 291-312 ◽  
Author(s):  
Yie-Chien Chen ◽  
Ching-Cheng Teng
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Structure ◽  
Model Reference ◽  
Model Reference Control ◽  
Fuzzy Neural

scholarly journals Model Reference Adaptive Control and Fuzzy Neural Network Synchronous Motion Compensator for Gantry Robots

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 123
Author(s):  
Chin-Sheng Chen ◽  
Nien-Tsu Hu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Fuzzy Neural Network ◽  
Model Reference Adaptive Control ◽  
Model Reference ◽  
Synchronous Motion ◽  
Fuzzy Neural ◽  
Gantry Robots ◽  
Model Reference Adaptive ◽  
Gantry Robot

A model reference adaptive control and fuzzy neural network (FNN) synchronous motion compensator for a gantry robot is presented in this paper. This paper proposes the development and application of gantry robots with MRAC and FNN online compensators. First, we propose a model reference adaptive controller (MRAC) under the cascade control method to make the reference model close to the real model and reduce tracking errors for the single axis. Then, a fuzzy neural network compensator for the gantry robot is proposed to compensate for the synchronous errors between the dual servo motors to improve precise movement. In addition, an online parameter training method is proposed to adjust the parameters of the FNN. Finally, the experimental results show that the proposed method improves the synchronous errors of the gantry robot and demonstrates the methodology in this paper. This study also successfully integrates the hardware and successfully verifies the proposed methods.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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