Single neuron self-tuning PID control for welding molten pool depth

Author

Liu, Xiwen

Author_Institution

Dept. of Mech. Eng., Xiangtan Univ., Xiangtan

fYear

2008

fDate

25-27 June 2008

Firstpage

7922

Lastpage

7925

Abstract

Welding molten pool depth control system based on single neuron self-tuning PID is designed, and several learning algorithms for neuron weights are simulated, the simulation results show that this controller reacts quickly and has good stability. Adopting the improved Hebb learning algorithm can bring best effect. The experiment with various cross-section and seam-gap workpiece is perfect, showing that single-neuron self-tuning PID controller is suitable to the complicated welding system.

Keywords

Hebbian learning; control system synthesis; neurocontrollers; self-adjusting systems; spatial variables control; stability; three-term control; welding; Hebb learning algorithm; seam-gap workpiece; single neuron self-tuning PID control; stability; welding molten pool depth control system; Artificial neural networks; Automatic control; Automation; Control systems; Error correction; Neurons; Pi control; Proportional control; Three-term control; Welding; Single-neuron; improved Hebb learning algorithm; self-tuning PID; welding molten pool depth;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4594166

Filename

4594166