Title :
Model updating and thruster fault diagnosis for underwater vehicle
Author :
Chu, Zhenzhong ; Zhang, Mingjun ; Wang, Yujia ; Song, Weixu
Author_Institution :
Coll. of Mech. & Electr. Eng., Harbin Eng. Univ., Harbin, China
Abstract :
As the impact of underwater vehicle dynamics modeling error on fault diagnosis system, a method using improved Elman neural network to modify underwater vehicle dynamics model in the current is proposed. The neural network parameter adjustment law under the Lyapunov stability is given. Sliding mode observer is constructed for state estimation based on the modified dynamics model. The change of state estimation residual of each DOF is analyzed when fault occurs in different thrusters of under vehicle. A thruster fault diagnosis method based on residual state fusion is presented, which has been validated through AUV sea trials data.
Keywords :
Lyapunov methods; fault diagnosis; mobile robots; neural nets; observers; remotely operated vehicles; underwater vehicles; variable structure systems; vehicle dynamics; AUV sea trial data; Lyapunov stability; improved Elman neural network; parameter adjustment law; residual state fusion; sliding mode observer; state estimation; thruster fault diagnosis; underwater vehicle dynamic modeling error; Artificial neural networks; Fault diagnosis; Observers; Underwater vehicles; Vehicle dynamics; Vehicles; fault diagnosis; fusion; neural networks; sliding mode observer; underwater vehicle;
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.2010.5554265
