Staff View
Integration of hardware and optimization control for robotic and prosthetic systems

Descriptive

TypeOfResource
Text
TitleInfo (ID = T-1)
Title
Integration of hardware and optimization control for robotic and prosthetic systems
Identifier
ETD_1567
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051343
Language
LanguageTerm (authority = ISO639-2)
eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Biomedical Engineering
Subject (ID = SBJ-1); (authority = ETD-LCSH)
Topic
Prosthesis
Subject (ID = SBJ-1); (authority = ETD-LCSH)
Topic
Robotics
Abstract
With medical advances through the past half century, survival rates following trauma have risen. Along with this rise has come an increase in the number of survivors with amputated limbs. Many of these survivors are Soldiers, Airmen, and Marines, who are relatively young and could benefit from sophisticated prostheses to replace the lost function.
These prostheses would be very maneuverable and able to better mimic the natural human motions. Such devices would likely be high degree of freedom with many actuators. Control of prostheses with intelligent algorithms may provide improved performance for the user.
To this end, a novel experimental transmission for driving the several joints of such a device has been developed and tested. Also, it has been used in the design, production, and testing of a 3 DOF digit actuator for use in a prosthetic hand.
Embedding the control hardware would make such a prosthesis more compact and portable. Using custom printed circuit boards, Microchip PIC microcontrollers have been used to control the digit actuator. Taking advantage of surface mount packages, control boards have been developed which integrate motor drivers with microcontrollers, and fit into a space comparable to that of the aforementioned prosthesis. Furthermore, the networking capability of these controllers has been demonstrated, presenting an extensible framework for addition of processing power as technology develops.
Given the non-linear nature of the several joints in the system, intelligent controls have been explored. Model reference adaptive control (MRAC) was used in simulation of digit models. Also, coupling MRAC with artificial neural networks yields ANN-MRAC (artificial neural network model reference adaptive control). Training these ANN control systems using ALOPEX yields good tracking performance across the non-linear range of the system. Such control logic may prove effective in a time varying non-linear system such as a hand prosthesis.
Human machine interfacing is key in the use of prostheses. Since a minimal amount of training is most desirable for the user, adaptive and intelligent methods may provide a control interface framework that reduces learning time for the user. To accomplish this, an algorithm for optimization of large dimensionality sensor grids was developed. This algorithm uses several template matrices to optimize the gain of each sensor in the grid. This both identifies a region of activity, and reduces the signal-to-noise ration of the sensor grid output by reducing gain on channels not containing information. The desired region is identified through enhancement of the signal gain on the sensors above the region. This would allow the placement of sensors on the body in an inexact fashion and instead let the computer optimize the sensor network gains for regions of activity associated with a given motion. Such an adaptive system reduces learning time for the user, thus reducing human error and easing use.
PhysicalDescription
Form (authority = gmd)
electronic resource
Extent
xv, 137 p. : ill.
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 129-136)
Note (type = statement of responsibility)
by Jeffrey Edward Erickson
Name (ID = NAME-1); (type = personal)
NamePart (type = family)
Erickson
NamePart (type = given)
Jeffrey Edward
Role
RoleTerm (authority = RULIB); (type = )
author
DisplayForm
Jeffrey Edward Erickson
Name (ID = NAME-2); (type = personal)
NamePart (type = family)
Tzanakou
NamePart (type = given)
Evangelia
Role
RoleTerm (authority = RULIB); (type = )
chair
Affiliation
Advisory Committee
DisplayForm
Evangelia Tzanakou
Name (ID = NAME-3); (type = personal)
NamePart (type = family)
Gajic
NamePart (type = given)
Zoran
Role
RoleTerm (authority = RULIB); (type = )
internal member
Affiliation
Advisory Committee
DisplayForm
Zoran Gajic
Name (ID = NAME-4); (type = personal)
NamePart (type = family)
Langrana
NamePart (type = given)
Noshir
Role
RoleTerm (authority = RULIB); (type = )
internal member
Affiliation
Advisory Committee
DisplayForm
Noshir Langrana
Name (ID = NAME-5); (type = personal)
NamePart (type = family)
DeLaurentis
NamePart (type = given)
Kathryn
Role
RoleTerm (authority = RULIB); (type = )
outside member
Affiliation
Advisory Committee
DisplayForm
Kathryn J DeLaurentis
Name (ID = NAME-1); (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB); (type = )
degree grantor
Name (ID = NAME-2); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB); (type = )
school
OriginInfo
DateCreated (point = ); (qualifier = exact)
2009
DateOther (qualifier = exact); (type = degree)
2009-05
Place
PlaceTerm (type = code)
xx
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T30G3KC1
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (AUTHORITY = GS); (ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
RightsEvent (AUTHORITY = rulib); (ID = 1)
Type
Permission or license
Detail
Non-exclusive ETD license
AssociatedObject (AUTHORITY = rulib); (ID = 1)
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Back to the top

Technical

ContentModel
ETD
MimeType (TYPE = file)
application/pdf
MimeType (TYPE = container)
application/x-tar
FileSize (UNIT = bytes)
8335360
Checksum (METHOD = SHA1)
b485e9ba586ea5fd02bad349eae9545d15a7ffbb
Back to the top
Version 8.4.8
Rutgers University Libraries - Copyright ©2022