Name
Abstract | ||
|---|---|---|
Unmanned underwater vehicles (UUVs) typically operate in uncertain and changing environments. Since the dynamics of UUVs are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high-performance control system of a UUV is needed to have the capacities of learning and adaptation to the variations in the UUV's dynamics. This paper presents the utilization of an adaptive neuro-control scheme as a controller for controlling a UUV in six degrees of freedom. No prior offline training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. Asymptotic convergence of the UUV's tracking errors and stability of the presented control system is guaranteed on the basis of the Lyapunov theory. In this paper, neural network architectures based on radial basis functions and multilayer structures have been used to evaluate the performance of the adaptive controller via computer simulation. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1080/00207720600566495 | Int. J. Systems Science |
Keywords | Field | DocType |
asymptotic convergence,adaptive controller,unmanned underwater vehicle,neural network,neural network control,control system,radial basis function,adaptive control,proposed scheme,adaptive neuro-control scheme,high-performance control system,degree of freedom,computer simulation,stability theory,neural networks,explicit knowledge | Lyapunov function,Control theory,Control theory,Six degrees of freedom,Control engineering,Autonomous system (mathematics),Control system,Adaptive control,Artificial neural network,Mathematics,Tracking error | Journal |
Volume | Issue | ISSN |
37 | 3 | 0020-7721 |
Citations | PageRank | References |
6 | 0.57 | 11 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vassilis S. Kodogiannis | 1 | 272 | 35.17 |