Position control of ultrasonic motors using dead-zone compensation with fuzzy neural network

2004 ◽  
Author(s):  
T. Senjyu ◽  
T. Yoshida ◽  
K. Uezato ◽  
S.K. Panda
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Position Control ◽  
Dead Zone ◽  
Ultrasonic Motors ◽  
Fuzzy Neural

Position Control of Ultrasonic Motors Using Dead-Zone Compensation with Fuzzy Neural Network

2006 ◽  
Vol 34 (11) ◽  
pp. 1253-1266 ◽  
Author(s):  
Tomohiro Yoshida ◽  
Tomonobu Senjyu ◽  
Mitsuru Nakamura ◽  
Naomitsu Urasaki ◽  
Hideomi Sekine ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Position Control ◽  
Dead Zone ◽  
Ultrasonic Motors ◽  
Fuzzy Neural

scholarly journals A Robust Adaptive Control using Fuzzy Neural Network for Robot Manipulators with Dead-Zone

2019 ◽  
Vol 14 (5) ◽  
pp. 692
Author(s):  
D. Ha Vu ◽  
Shoudao Huang ◽  
T. Diep Tran ◽  
T. Yen Vu ◽  
V. Cuong Pham
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Dead Zone ◽  
Approximation Error ◽  
Robust Adaptive Control ◽  
Neural Network Controller ◽  
Fuzzy Neural ◽  
Robust Adaptive

In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) is proposed to dead the unknown model and external disturbance. Here, the unknown dynamics of the robot system is deal by using fuzzy neural network to approximate the unknown dynamics. The online training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this proposal, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of RAFNNs for RM are guaranteed. The simulations and experiments performed on three-link RM are provided in comparison with neural-network (NNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the RAFNNs.

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