Modeling of switched reluctance machine with few samples based on chaotic fuzzy neural network

2015 ◽  
Author(s):  
Shoujun Song ◽  
Lefei Ge
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Switched Reluctance Machine ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Fuzzy Neural

Modeling of Switched Reluctance Motor Based on Dynamic Fuzzy Neural Network

2012 ◽  
Vol 220-223 ◽  
pp. 665-668 ◽  
Author(s):  
Ai De Xu ◽  
Shan Shan Zhang ◽  
Di Sun
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Switched Reluctance Motor ◽  
Mathematic Model ◽  
Modeling Method ◽  
System Structure ◽  
Actual System ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Fuzzy Neural

This paper proposed a novel mathematic model for switched reluctance motor(SRM):dynamic fuzzy neural network(D-FNN) was used to model for SRM based on the inductance characteristics, namely experimentally measured sample data. Compared with other modeling method, the inductance based on D-FNN can be trained on line and has the advantages of compact system structure and strong generalization ability. The SRM system is simulated with the trained inductance model. Compared with the actual system, the current waves are similar. This proves the new modeling method is correct and feasible.

Moore-Penrose pseudo-inverse and artificial neural network modeling in performance prediction of switched reluctance machine

2020 ◽  
Vol 39 (6) ◽  
pp. 1411-1430
Author(s):  
Ana Camila Ferreira Mamede ◽  
José Roberto Camacho ◽  
Rui Esteves Araújo ◽  
Igor Santos Peretta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Computational Cost ◽  
Neural Network Modeling ◽  
Switched Reluctance Machine ◽  
Data Set ◽  
Content Type ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Artificial Neural

Purpose The purpose of this paper is to present the Moore-Penrose pseudoinverse (PI) modeling and compare with artificial neural network (ANN) modeling for switched reluctance machine (SRM) performance. Design/methodology/approach In a design of an SRM, there are a number of parameters that are chosen empirically inside a certain interval, therefore, to find an optimal geometry it is necessary to define a good model for SRM. The proposed modeling uses the Moore-Penrose PI for the resolution of linear systems and finite element simulation data. To attest to the quality of PI modeling, a model using ANN is established and the two models are compared with the values determined by simulations of finite elements. Findings The proposed PI model showed better accuracy, generalization capacity and lower computational cost than the ANN model. Originality/value The proposed approach can be applied to any problem as long as experimental/computational results can be obtained and will deliver the best approximation model to the available data set.

Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor

2012 ◽  
pp. 387-398
Author(s):  
Baoming Ge ◽  
Aníbal T. de Almeida
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Robot Manipulator ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Fuzzy Neural

Applications of switched reluctance motor (SRM) to direct drive robot are increasingly popular because of its valuable advantages. However, the greatest potential defect is its torque ripple owing to the significant nonlinearities. In this paper, a fuzzy neural network (FNN) is applied to control the SRM torque at the goal of the torque-ripple minimization. The desired current provided by FNN model compensates the nonlinearities and uncertainties of SRM. On the basis of FNN-based current closed-loop system, the trajectory tracking controller is designed by using the dynamic model of the manipulator, where the torque control method cancels the nonlinearities and cross-coupling terms. A single link robot manipulator directly driven by a four-phase 8/6-pole SRM operates in a sinusoidal trajectory tracking rotation. The simulated results verify the proposed control method and a fast convergence that the robot manipulator follows the desired trajectory in a 0.9-s time interval.

Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor

2011 ◽  
Vol 5 (3) ◽  
pp. 86-98
Author(s):  
Baoming Ge ◽  
Aníbal T. de Almeida
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Robot Manipulator ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Fuzzy Neural

Applications of switched reluctance motor (SRM) to direct drive robot are increasingly popular because of its valuable advantages. However, the greatest potential defect is its torque ripple owing to the significant nonlinearities. In this paper, a fuzzy neural network (FNN) is applied to control the SRM torque at the goal of the torque-ripple minimization. The desired current provided by FNN model compensates the nonlinearities and uncertainties of SRM. On the basis of FNN-based current closed-loop system, the trajectory tracking controller is designed by using the dynamic model of the manipulator, where the torque control method cancels the nonlinearities and cross-coupling terms. A single link robot manipulator directly driven by a four-phase 8/6-pole SRM operates in a sinusoidal trajectory tracking rotation. The simulated results verify the proposed control method and a fast convergence that the robot manipulator follows the desired trajectory in a 0.9-s time interval.

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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