Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

2011 ◽  
Vol 383-390 ◽  
pp. 290-296
Author(s):  
Yong Hong Zhu ◽  
Wen Zhong Gao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Wavelet Neural Network ◽  
Adaptive Robust Control ◽  
Loop Control ◽  
Control Approach ◽  
Mimo Nonlinear Systems ◽  
Output Tracking Control ◽  
Dynamic Inverse

Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

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.

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