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Informations générales |
| Auteur |
Tian, Jingwen; Gao, Meijuan; He, Yonggang |
| Publié |
InTech Open Access Publisher, 2013
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| Abstract |
Since the control system of the welding gun pose
in whole‐position welding is complicated and nonlinear,
an intelligent control system of welding gun pose for a
pipeline welding robot based on an improved radial basis
function neural network (IRBFNN) and expert system (ES)
is presented in this paper. The structure of the IRBFNN is
constructed and the improved genetic algorithm is adopted
to optimize the network structure. This control system
makes full use of the characteristics of the IRBFNN and the
ES. The ADXRS300 micro‐mechanical gyro is used as the
welding gun position sensor in this system. When the
welding gun position is obtained, an appropriate pitch
angle can be obtained through expert knowledge and the
numeric reasoning capacity of the IRBFNN. ARM is used
as the controller to drive the welding gun pitch angle step
motor in order to adjust the pitch angle of the welding gun
in real‐time. The experiment results show that the
intelligent control system of the welding gun pose using
the IRBFNN and expert system is feasible and it enhances
the welding quality. This system has wide prospects for
application. |
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International Journal of Advanced Robotic Systems
Auteur: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publicado: 2004
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