ACTIVE HEAT DISSIPATION SYSTEM USING ADAPTIVE RECURRENT WAVELET NEURAL NETWORK CONTROL

2016 ◽  
Vol 40 (4) ◽  
pp. 445-456
Author(s):  
Yung-Lung Lee ◽  
Shou-Jen Hsu ◽  
Yen-Bin Chen
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Heat Dissipation ◽  
Intelligent System ◽  
Descent Method ◽  
Wavelet Neural Network ◽  
Steepest Descent Method ◽  
Neural Network Controller ◽  
Network Controller ◽  
Voltage Saturation

This paper proposes a novel cooling control system with the intelligent active technique, which is based on the NI-PXI systems structure combined with the heat pipe cooling chips. In order to further solve the controlling problems of nonlinear heat transfer system, the proposed intelligent system involves the PID control, traditional control, and the adaptive recurrent wavelet neural network controller (ARWNNC) control techniques. The traditional control there exists the undesirable control chattering, The PID control the Response of Control cannot be processed immediately and the input voltage saturation phenomenon, the adaptive recurrent wavelet neural network controller, is employed to approximate the ideal controller, while the corresponding parameters are derived by the gradient steepest descent method, thus being provided with the adaptive real-time control ability.

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.

scholarly journals An Adaptive Neural Network Controller Based on PSO and Gradient Descent Method for PMSM Speed Drive

2018 ◽  
Vol 9 (3) ◽  
pp. 1412
Author(s):  
Zribi Ali ◽  
Zaineb Frijet ◽  
Mohamed Chtourou
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Control Method ◽  
Speed Control ◽  
Pso Algorithm ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller

In this paper, based on the combination of particle swarm optimization (PSO) algorithm and neural network (NN), a new adaptive speed control method for a permanent magnet synchronous motor (PMSM) is proposed. Firstly, PSO algorithm is adopted to get the best set of weights of neural network controller (NNC) for accelerating the convergent speed and preventing the problems of trapping in local minimum. Then, to achieve high-performance speed tracking despite of the existence of varying parameters in the control system, gradient descent method is used to adjust the NNC parameters. The stability of the proposed controller is analyzed and guaranteed from Lyapunov theorem. The robustness and good dynamic performance of the proposed adaptive neural network speed control scheme are verified through computer simulations.

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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