An artificial neural network model for backside bead width was established and three control methods——PID, fuzzy and neuron were designed, simulated and tested. The test results of bead on plate weld of GTAW indicat...An artificial neural network model for backside bead width was established and three control methods——PID, fuzzy and neuron were designed, simulated and tested. The test results of bead on plate weld of GTAW indicate that the artificial neural network (ANN) modeling and learning control method have more advantages than the conventional method. They show that the ANN modeling and learning control method is an effective approach to real time control of welding dynamics and ideal quality.展开更多
In this article an artificial neural network （ANN） has been designed for the control of DC series motor through a DC chopper （DC-DC buck converter）. The proportional-integral-derivative （PID）-ANN speed controlle...In this article an artificial neural network （ANN） has been designed for the control of DC series motor through a DC chopper （DC-DC buck converter）. The proportional-integral-derivative （PID）-ANN speed controller controls the motor voltage by controlling the duty cycle of the chopper thereby the motor speed is regulated. The PID-ANN controller performances are analyzed in both steady-state and dynamic operating condition with various set speeds and various load torques. The rise time, maximum overshoot, settling time, steady-state error, and speed drops are taken for comparison with conventional PID controller and existing work. The training samples for the neuron controller are acquired from the conventional PID controller. The PID-ANN controller performances are analyzed in respect of various load torques and various speeds using MATLAB simula-tion. Then the designed controllers were experimentally verified using an NXP 80C51 based microcontroller （P89VSIRD2BN）. It was found that the hybrid PID-ANN controller with DC chopper can have better control compared with conventional PID controller.展开更多
文摘An artificial neural network model for backside bead width was established and three control methods——PID, fuzzy and neuron were designed, simulated and tested. The test results of bead on plate weld of GTAW indicate that the artificial neural network (ANN) modeling and learning control method have more advantages than the conventional method. They show that the ANN modeling and learning control method is an effective approach to real time control of welding dynamics and ideal quality.
文摘In this article an artificial neural network （ANN） has been designed for the control of DC series motor through a DC chopper （DC-DC buck converter）. The proportional-integral-derivative （PID）-ANN speed controller controls the motor voltage by controlling the duty cycle of the chopper thereby the motor speed is regulated. The PID-ANN controller performances are analyzed in both steady-state and dynamic operating condition with various set speeds and various load torques. The rise time, maximum overshoot, settling time, steady-state error, and speed drops are taken for comparison with conventional PID controller and existing work. The training samples for the neuron controller are acquired from the conventional PID controller. The PID-ANN controller performances are analyzed in respect of various load torques and various speeds using MATLAB simula-tion. Then the designed controllers were experimentally verified using an NXP 80C51 based microcontroller （P89VSIRD2BN）. It was found that the hybrid PID-ANN controller with DC chopper can have better control compared with conventional PID controller.
