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Using a cerebellar model arithmetic computer (CMAC) neural network to control an autonomous underwater vehicle

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Date Issued:
1991
Summary:
The design of an Autonomous Undersea Vehicle (AUV) control system is a significant challenge in-light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This thesis describes a Neural Network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an Autonomous Underwater Vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six output parameters; forward velocity, vertical velocity, pitch angle, side velocity, roll angle, and yaw angle. Properties of CMAC and results of computer simulations for identification and control of the AUV model are presented.
Title: Using a cerebellar model arithmetic computer (CMAC) neural network to control an autonomous underwater vehicle.
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Name(s): Comoglio, Rick F.
Florida Atlantic University, Degree grantor
Pandya, Abhijit S., Thesis advisor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1991
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 145 p.
Language(s): English
Summary: The design of an Autonomous Undersea Vehicle (AUV) control system is a significant challenge in-light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This thesis describes a Neural Network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an Autonomous Underwater Vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six output parameters; forward velocity, vertical velocity, pitch angle, side velocity, roll angle, and yaw angle. Properties of CMAC and results of computer simulations for identification and control of the AUV model are presented.
Identifier: 14762 (digitool), FADT14762 (IID), fau:11553 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.C.E.)--Florida Atlantic University, 1991.
Subject(s): Neural networks (Computer science)
Artificial intelligence
Submersibles--Automatic control
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/14762
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.