DocumentCode :
2843685
Title :
Research on intelligent control strategy of plasma cutting process
Author :
Jia, Deli ; He, Jinsong
Author_Institution :
Harbin Univ. of Sci. & Technol., Harbin, China
fYear :
2010
fDate :
26-28 May 2010
Firstpage :
3409
Lastpage :
3413
Abstract :
By taking into account these problems such as the nonlinear volt-ampere property of plasma arc, the multi-parameter close coupling of cutting technology process and the difficulty to determine the optimal parameter, we present a control strategy based on the combination of RBF neural network and expert system. The RBF neural network and expert system can resolve a certain tape problems respectively. Mutual combination can take full advantages of the high logical reasoning ability of expert system and the good robustness and a strong learning ability of neural network, overcoming the disadvantages of the expert system as poor fault tolerance and learning ability. This control algorithm avoids the redundant modeling process of nonlinear system, having the ability of multi-parameter decoupling. Expert system has high adaptive ability and self-learning ability so it can achieve parameters trained by neural network during the reasoning process and then it obtain optimal output value, having quite strong guidance to productive practice.
Keywords :
cutting; expert systems; intelligent control; learning systems; radial basis function networks; RBF neural network; cutting technology process; expert system; high logical reasoning ability; intelligent control strategy; learning ability; multiparameter close coupling; nonlinear volt-ampere property; plasma cutting process; Control systems; Couplings; Expert systems; Fault tolerant systems; Intelligent control; Neural networks; Nonlinear control systems; Optimal control; Plasma properties; Robustness; RBF neural network; expert system; multi-parameter close coupling; optimal parameter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
Type :
conf
DOI :
10.1109/CCDC.2010.5498585
Filename :
5498585
Link To Document :
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