Title :
Decoupling control of forging machine based on PID neural network
Author :
Zhang, Dapeng ; Zhai, Wenpeng ; Wu, Aiguo ; Du, Chunyan
Author_Institution :
Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin, China
Abstract :
A PID neural network is proposed to solve the decoupling problem of multi-cylinder forging machine. Based on analyzing forging machine´s model the PID neural network is determined as 10-15-5 structure. 10 input- neurons connect the given value and the measure value of left front cylinder, left posterior cylinder, right front cylinder, right posterior cylinder and main cylinder.15 neurons of hidden layer respond 5 groups of P-neuron, I-neuron and D-neuron. And 5 output-neurons connect 5 circuit controllers. The learning process uses BP algorithm. The simulation shows this approach is effective.
Keywords :
backpropagation; forging; neurocontrollers; three-term control; BP algorithm; PID neural network; decoupling control; learning process; multicylinder forging machine; posterior cylinder; Artificial neural networks; Cavity resonators; Control systems; Mathematical model; Neurons; Petroleum; Valves; Decoupling; Forging machine; PID neural network;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554480