Self-adaptive neuro-fuzzy control with fuzzy basis function network for autonomous underwater vehicles

Jeen Shing Wang, George C.S. Lee, Junku Yuh

Research output: Contribution to conferencePaperpeer-review

11 Citations (Scopus)

Abstract

This paper presents an on-line self-adaptive neuro-fuzzy control that serves as a better alternative control scheme in controlling Autonomous Underwater Vehicles (AUVs) in an uncertain and unstructured environment. The proposed self-adaptive neuro-fuzzy controller is a five-layer feedforward neural network that implements fuzzy basis function (FBF) expansions and is capable of self-constructing and self-restructuring its internal node connectivity and learning the parameters of each node based on incoming training data. Computer simulations have been conducted to validate the performance of the proposed neuro-fuzzy controller and an experimental verification has been scheduled to verify it on ODIN, an autonomous underwater vehicle developed at the University of Hawaii.

Original languageEnglish
Pages130-135
Number of pages6
Publication statusPublished - 1999
Event1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients' - Kyongju, South Korea
Duration: 1999 Oct 171999 Oct 21

Other

Other1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients'
CityKyongju, South Korea
Period99-10-1799-10-21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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