Design of an intelligent control system for remotely operated vehicles

Author

Yuh, J. ; Lakshmi, R.

Author_Institution

Robotics Lab., Hawaii Univ., Honolulu, HI, USA

fYear

1991

fDate

15-17 Aug 1991

Firstpage

151

Lastpage

159

Abstract

The application of a neural network controller is described. Three learning algorithms for online implementation of the controller are discussed. These control schemes do not require any information about the system dynamics except an upper bound of the inertia terms. Selection of the number of layers in the neural network, the number of neurons in the hidden layer, initial weights for the network, and the critic coefficient was done based on the results of preliminary tests. The performances of the three learning algorithms were compared. The effectiveness of the neural net controller in handling time-varying parameters and random noise was tested by a case study on a remotely operated vehicle (ROV) system for robotic underwater operations. The results of the comparisons and the testing are presented in detail

Keywords

marine systems; mobile robots; neural nets; time-varying systems; ROV; hidden layer; inertia terms; intelligent control system; learning algorithms; neural network controller; random noise; remotely operated vehicles; time-varying parameters; upper bound; Control systems; Intelligent control; Neural networks; Neurons; Remotely operated vehicles; Robots; System testing; Time varying systems; Upper bound; Vehicle dynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Ocean Engineering, 1991., IEEE Conference on

Conference_Location

Washington, DC

Print_ISBN

0-7803-0205-2

Type

conf

DOI

10.1109/ICNN.1991.163341

Filename

163341