Abstract
A third generation wide-band-gap SiC semiconductor device is used in the SiC arc welding power source, which has a higher inverter frequency and greatly improves the dynamic characteristics of the arc welding power source, providing opportunities for control algorithm optimization. A composite control method of the arc welding power source combining the expert system and single neuron Proportional-Integral-Derivative (PID) is proposed in this paper, aimed at the fact that the proportional cofficient of neuron PID can hardly be adapted to rapid welding current changes. The SiC arc welding power source is taken as the plant of study in this paper. A mathematical model of the arc welding power source-arc system is established, and the controller of the arc welding power source based on the neuron PID and corresponding expert rules are defined to adjust the proportional coefficient of neuron PID Finally, the neuron PID controller (SNC) and the composite controller based on the expert system and neuron PID (ESNC) are simulated and verified. The results show that compared with the neuron PID algorithm, this method can adjust the proportional neuron PID coefficient in real time according to the welding current and has a better adaptive ability and superior tracking performance for arc welding power source control.
Highlights
IntroductionMany scholars have studied a variety of improved PID algorithms
Theband gap of a SiC power device is much wider than that of a Si-based power device, and the switching speed is higher, which provides better high-frequency characteristics for the arc welding power source
A new generation of wide-bandgap welding power sources based on SiC power devices have been developed, as reported in reference [3], whose inverter frequency can be as high as 200 kHz; a full-bridge resonant converter with a new SiC power device, whose main circuit adopts an LLC topology structure and whose resonant converter frequency ranges from 260- to 310 kHz, has been established in reference [4]
Summary
Many scholars have studied a variety of improved PID algorithms. It is pointed out in reference [8] that when the working area is frequently disturbed, artificial intelligence technology such as neural networks, fuzzy control or expert controllers can be used to optimize the adaptive welding parameters in real time and significantly reduce welding defects. The application of a neuron PID controller with a strong robustness and adaptability is beneficial to further improve the control effect on an arc welding power source [11]. The simulation results in reference [12] reveal that the pulse current of the welding power source operated by a single neuron adaptive PID controller has a low intensity when the peak and base values are superposed. There are few neuron PID improvement algorithms that can be applied to ultrahigh inverter frequency arc welding power sources
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