Title
Neural-network-based adaptive observer design for autonomous underwater vehicle in shallow water
Abstract
It is often unavailable to obtain direct measurements of the underwater vehicles' velocities in actual implementations. A neural-network-based adaptive observer system is designed to solve this problem in this paper. Since the dynamics of autonomous underwater vehicle (AUV) are highly nonlinear nature and the hydrodynamic coefficients are difficult to be accurately estimated, a dynamic recurrent fuzzy neural network (DRFNN) is employed in the observer to estimate the unknown nonlinear characteristics in the vehicles' dynamics. The proposed observer can estimate AUV's low-frequency motion and slowly varying environmental disturbance from the measuring signals, which include high-frequency motion signals and the noise of sonar. The network weights adaptation law are derived from the Lyapunov stability analysis. With the Lyapunov stability theory, the convergence of these estimations is global and exponential.
Year
DOI
Venue
2013
10.1109/ICNC.2013.6817973
ICNC
Keywords
Field
DocType
observers,network weights adaptation law,neural network,nonlinear nature,slowly varying environmental disturbance,unknown nonlinear characteristics,hydrodynamic coefficients,auv,motion control,high-frequency motion signals,neurocontrollers,control system synthesis,low-frequency motion,autonomous underwater vehicle,autonomous underwater vehicles,velocity control,adaptive control,nonlinear control systems,hydrodynamics,lyapunov stability analysis,shallow water,vehicle dynamics,observer,stability,drfnn,neural-network-based adaptive observer design,dynamic recurrent fuzzy neural network,lyapunov methods,underwater vehicle velocities,vectors,neural networks,dynamics
Convergence (routing),Exponential function,Nonlinear system,Computer science,Control theory,Lyapunov stability,Sonar,Observer (quantum physics),Artificial neural network,Underwater
Conference
Citations 
PageRank 
References 
1
0.37
4
Authors
3
Name
Order
Citations
PageRank
G. Xia156.18
Chengcheng Pang231.06
Ju Liu310.37