Comparison of neural networks based direct inverse control systems for a double propeller boat model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This paper presents the thorough evaluation and analysis on the direct inverse neural networks based controller systems for a double-propeller boat model. Two direct inverse controller systems that were designed with and without feedback were implemented on a double propeller boat model using two neural networks based control approaches, namely the back-propagation based neural controller (BPNN-controller) and the selforganizing maps based neural controller (SOM-controller). Then, the resulted control errors of the systems were compared. Simulation results revealed that the direct inverse control without feedback produced lower error compared to the direct inverse control with feedback. Another important finding from the study was that the SOM-controller is superior to the BPNN-controller in terms of control error and training computational cost.

Original languageEnglish
Title of host publicationProceedings of 2016 5th International Conference on Network, Communication and Computing, ICNCC 2016
PublisherAssociation for Computing Machinery
Pages310-315
Number of pages6
ISBN (Electronic)9781450347938
DOIs
Publication statusPublished - 17 Dec 2016
Event5th International Conference on Network, Communication and Computing, ICNCC 2016 - Kyoto, Japan
Duration: 17 Dec 201621 Dec 2016

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Network, Communication and Computing, ICNCC 2016
CountryJapan
CityKyoto
Period17/12/1621/12/16

Keywords

  • Backpropagation
  • Boat control system
  • Direct inverse control
  • Neural network controller
  • Self-organizing maps

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