A neural network controller for unmanned underwater vehicles

Title:

A neural network controller for unmanned underwater vehicles

Creator:

Kodogiannis, V. and Tomtsis, D.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:98c027ae-e80c-407f-82e3-daf8196cb1c8
uuid:98c027ae-e80c-407f-82e3-daf8196cb1c8

Subject:

neural networks, underwater vehicles, stability theory, adaptive control, and marine systems

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Underwater robotic vehicles have become an important tool for various underwater tasks because they háve greater speed, endurance, depth capability, and safety than human divers. The problem of controlling a remotely operated underwater vehicle in 6 degrees of freedom (DOF) is addressed in this paper, as an example of a system containing severe non-linearities. Neural networks are been used in a closed-loop to approximate the nonlinear vehicle dynamics. No prior off-line 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. A control law and a stable on-line adaptive law are derived using the Lyapunov theory, and the convergence of the tracking error to zero and the bounded-ness of signals are guaranteed by applying Barbalaťs Lyapunov-like lemma. In this páper, a neural network architecture based on radial basis functions has been ušed to evaluate the performance of the proposed adaptive controller for the motion of the Norwegian Experimental Remotely Operated Vehicle (NEROV).

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2001 Volume:11 | Number:3

Harvested from:

CDK

Metadata only:

false