| Issue |
E3S Web Conf.
Volume 233, 2021
2020 2nd International Academic Exchange Conference on Science and Technology Innovation (IAECST 2020)
|
|
|---|---|---|
| Article Number | 04001 | |
| Number of page(s) | 5 | |
| Section | MEA2020-Mechanical Engineering and Automation | |
| DOI | https://doi.org/10.1051/e3sconf/202123304001 | |
| Published online | 27 January 2021 | |
Composite control of the SiC arc welding power source based on the expert system and neuron PID
1 School of Electrical Technology, GuangDong Mechanical & Electrical Polytechnic, GuangZhou, 510550
2 School of Mechanical and Automotive Engineering, South China University of Technology, GuangZhou, 510640
* Corresponding author email: 734380844@qq.com
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.
© The Authors, published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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