Title :
Single neuron self-tuning PID control for welding molten pool depth
Author_Institution :
Dept. of Mech. Eng., Xiangtan Univ., Xiangtan
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;
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
DOI :
10.1109/WCICA.2008.4594166
