DocumentCode :
2083570
Title :
Sliding mode control of ROV based on RBF neural networks adaptive learning
Author :
Liu, Heping ; Gong, Zhenbang ; Li, Min
Author_Institution :
Dept. of Precision Machinery, Shanghai Univ., Shanghai, China
Volume :
1
fYear :
2008
fDate :
17-19 Nov. 2008
Firstpage :
590
Lastpage :
594
Abstract :
This paper deals with a variable structure sliding mode control of ROV (remotely operated vehicle), with which the adaptive learning of the RBF neural network is used to estimate and approach the upper bound of the uncertainty and disturbance induced by hydrodynamics so as to avoid the difficulties of establishment and resolving of precision dynamic model. According the description and setting up of the control model, a tracking simulation was carried out and a series of tests on the yaw of ROV were performed in static pool. It is proved that this control strategy is available for the ROV.
Keywords :
control engineering computing; hydrodynamics; learning (artificial intelligence); radial basis function networks; remotely operated vehicles; underwater vehicles; variable structure systems; RBF neural network; ROV; adaptive learning; hydrodynamics; remotely operated vehicle; uncertainty; variable structure sliding mode control; Adaptive control; Adaptive systems; Hydrodynamics; Neural networks; Programmable control; Remotely operated vehicles; Sliding mode control; Uncertainty; Upper bound; Vehicle dynamics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-2196-1
Electronic_ISBN :
978-1-4244-2197-8
Type :
conf
DOI :
10.1109/ISKE.2008.4730999
Filename :
4730999
Link To Document :
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