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.

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.

The Intelligent Scheduling Method of Elevator Group Control Based on Fuzzy Neural Network

2012 ◽  
Vol 155-156 ◽  
pp. 653-657
Author(s):  
Yu Lin Dong ◽  
Xiao Ming Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Scheduling Algorithm ◽  
Mathematic Model ◽  
Group Control ◽  
Fuzzy Neural ◽  
And Performance ◽  
Elevator Group Control

Elevator group control system (EGCS) is a complex optimization system, which has the characteristics of multi-objective, uncertain, stochastic random decision-making and nonlinear. It is hard to describe the elevator group control system in exact mathematic model and to increase the capability of the system with traditional control method. In this paper, we aim at the characters of elevator group control system and intelligent control, introduce the system's control fashion and performance evaluate guidelines and propose an elevator group control scheduling algorithm based on fuzzy neural network.

Sign in / Sign up

Export Citation Format

Share Document