Torque ripple minimization in switched reluctance motors using fuzzy-neural network inverse learning control

2004 ◽  
Author(s):  
Zheng Hongtao ◽  
Lin Feng ◽  
Liu Liangen ◽  
Jiang Jingping ◽  
Xu Dehong
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Control ◽  
Torque Ripple ◽  
Switched Reluctance Motors ◽  
Switched Reluctance ◽  
Fuzzy Neural

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.

Self-Learning Control of PMA-Actuated Knee-Joint Rehabilitation Training Device Based on Fuzzy Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 1645-1650
Author(s):  
Xiao Li ◽  
Xia Hong ◽  
Ting Guan
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Learning Control ◽  
Training Device ◽  
Rehabilitation Training ◽  
Fuzzy Neural ◽  
Self Learning

To solve the problem of the delay, nonlinearity and time-varying properties of PMA-actuated knee-joint rehabilitation training device, a self-learning control method based on fuzzy neural network is proposed in this paper. A self-learning controller was designed based on the combination of pid controller, feedforward controller, fuzzy neural network controller, and learning mechanism. It was applied to the isokinetic continuous passive motion control of the PMA-actuated knee-joint rehabilitation training device. The experiments proved that the self-learning controller has the properties of high control accuracy and unti-disturbance capability, comparing with pid controller. This control method provides the beneficial reference for improving the control performance of such system.

Backstepping Adaptive Iterative Learning Control for Robotic Systems

2013 ◽  
Vol 284-287 ◽  
pp. 1759-1763
Author(s):  
Ying Chung Wang ◽  
Chiang Ju Chien ◽  
Chi Nan Chuang
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Fuzzy Neural Network ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Robotic Systems ◽  
Adaptive Iterative Learning Control ◽  
Repetitive Tasks ◽  
Fuzzy Neural

A backstepping adaptive iterative learning control for robotic systems with repetitive tasks is proposed in this paper. The backstepping-like procedure is introduced to design the AILC. A fuzzy neural network is applied for compensation of the unknown certainty equivalent controller. Using a Lyapunov like analysis, we show that the adjustable parameters and internal signals remain bounded, the tracking error will asymptotically converge to zero as iteration goes to infinity.

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