TIG Auto Welding Process Using Adaptive Wavelet Neural Network Controller

2015 ◽  
Vol 764-765 ◽  
pp. 634-639
Author(s):  
Yen Bin Chen ◽  
Yung Lung Lee ◽  
Shou Jen Hsu ◽  
Chin Chun Chang ◽  
Yi Wei Chen
Keyword(s):  
Neural Network ◽  
Vacuum Chamber ◽  
Welding Current ◽  
Wavelet Neural Network ◽  
Feed Speed ◽  
Automatic Welding ◽  
Adaptive Wavelet ◽  
Neural Network Controller ◽  
Network Controller ◽  
Wire Feed Speed

The study proposed adaptive wavelet neural network controller can achieve good and precise welding control performance and use synchrotron radiation research center developed multi-gun group automatic welding system to verify the validity of the research method. Multi-gun group welding system is applied in Taiwan Photon Source (TPS). Storage ring aluminum alloy vacuum chamber of Taiwan Photon Source .In the past aluminum alloy vacuum chamber welding, it all depends on the empirical welding rule of operator to give appropriate welding current, argon flow, wire feed speed and welding speed for control. Therefore, the paper uses automatic welding skill, which takes National Instruments PXI-8180 system as basic structure, and adaptive wavelet neural network controlled four optimized parameters, I.E. welding current, wire feed speed, flow rate of argon gas and welding speed, The vacuum chamber pressure value is also up to 6.2X10-10Torr/mA. It is successfully applied to the TPS system. Therefore, it can prove the effectiveness and practicality of the method proposed in this study.

DESIGN AN ADAPTIVE WAVELET NEURAL NETWORK CONTROLLER FOR A TWO-WHEELED MOBILE ROBOT LIKE VEHICLE

2014 ◽  
Vol 23 (09) ◽  
pp. 1450133 ◽  
Author(s):  
ALIREZA SAFA ◽  
GHASEM ALIZADEH ◽  
HAMID SHIRI
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Design Method ◽  
External Disturbance ◽  
Approximation Error ◽  
Wavelet Neural Network ◽  
Wheeled Mobile Robot ◽  
Adaptive Backstepping ◽  
Adaptive Wavelet ◽  
Neural Network Controller

This paper presents an analytical approach to design an adaptive backstepping wavelet neural network (WNN)-based controller for global asymptotic stabilization of a two-wheeled mobile robot (TWMR). It is assumed that the dynamics model is unknown, and also system exposed to an external disturbance. The design method is based on the concept of backstepping method. At the first level, adaptive backstepping controller is employed. This controller is taking advantage of WNN identifier for estimating of unknown plant dynamics. Moreover, since the adaption laws of controller are extracted in sense of Lyapunov function, the stability of closed loop is guaranteed. At the second level, robust controller is combined with primary controller, which results L2 tracking performance and comforts lumped uncertainties exit in control system due to approximation error and external disturbance. Finally, a numerical example for the proposed control scheme is presented.

Study on Genetic Fuzzy Wavelet Neural Network Controller of Robotic Manipulator

2012 ◽  
Vol 605-607 ◽  
pp. 1619-1624
Author(s):  
Yong Lin Wang ◽  
Dong Yun Wang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Tracking Control ◽  
Controller Design ◽  
Robotic Manipulator ◽  
Wavelet Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Tracking Controller ◽  
Fuzzy Wavelet Neural Network

This paper deals with the tracking controller design of robotic manipulator using genetic algorithm (GA). A genetic fuzzy wavelet neural network (GFWNN) controller is designed and implemented based on MATLAB in this paper, whose parameters are optimized by GA. The structure and algorithm of fuzzy wavelet neural network (FWNN) are described at first. Then the key content of GA used in this paper and the steps for using GA to optimize FWNN are demonstrated. Finally, a numerical simulation of tracking control for 2-link robotic manipulator is given to verify the effectiveness of the proposed method.

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