This paper investigates the use of a multilayered perceptron (MLP) network in controlling an unmanned underwater vehicle over a sea-bed profile and compares the use of applying chemotaxis learning to that of the more commonly employed back propagation algorithm. The reults show that, for differing sized MLPs, the chemotaxis algorithm produces a succesful controller over the sea-bed profile in an improved training time. Also it will be shown that, in the presence of noise and change in vehicle mass, the neural controller out-performed a classical proportional-integral-derivative controller


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A neural auto-depth controller for an unmanned underwater vehicle


    Additional title:

    Eine neurale Regeleinrichtung fuer die Tiefensteuerung eines unbemanntenUnterwasserfahrzeuges


    Contributors:
    Sutton, R. (author)

    Published in:

    The Journal of Navigation ; 50 , 2 ; 292-302


    Publication date :

    1997


    Size :

    11 Seiten, 7 Bilder, 5 Tabellen, 10 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





    Review on Auto-Depth Control System for an Unmanned Underwater Remotely Operated Vehicle (ROV) using Intelligent Controller

    Mohd Aras, Mohd Shahrieel / Abdullah, Shahrum Shah / Azis, F.A | BASE | 2015

    Free access

    Performance Analysis of PID and Fuzzy Logic Controller for Unmanned Underwater Vehicle for Depth Control

    Mohd Aras, Mohd Shahrieel / Sulaiman, Marizan / Keong, Yeoh Eik et al. | BASE | 2017

    Free access

    UNMANNED UNDERWATER VEHICLE

    ITEM ERIK F / LANGELIER MARK S / CARLSTEN CURTIS B et al. | European Patent Office | 2018

    Free access

    Unmanned underwater vehicle

    SMITH JEFFREY M / GODIN SAMUEL D / BAKER LEONARD M et al. | European Patent Office | 2020

    Free access