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dc.contributor.advisorRoemer, R. B.en_US
dc.contributor.authorKress, Reid Leonard.
dc.creatorKress, Reid Leonard.en_US
dc.date.accessioned2011-10-31T17:08:05Zen
dc.date.available2011-10-31T17:08:05Zen
dc.date.issued1988en_US
dc.identifier.urihttp://hdl.handle.net/10150/184430en
dc.description.abstractThe purpose of this research was to develop three real-time adaptive temperature controllers for hyperthermia heating systems. Each scheme is made adaptive by using a transient Gaussian estimation routine to estimate the tissue blood perfusion and by then using these estimated values either in an optimizing routine, or in an observer, or in both. The optimizing routine uses a steady-state Gaussian estimation technique to optimize the power distribution until the best possible match is obtained between the steady-state temperatures predicted by a treatment model and a prespecified ideal temperature distribution. The observer uses a treatment model to control unmeasured locations. The first adaptive control scheme uses the optimizing routine alone, the second uses the observer alone and the third uses both the optimzing routine and observer. The performance of each of the adaptive control schemes is compared to a standard proportional-integral-derivative (PID) control scheme for one-dimensional simulations of typical treatments. Results comparing the deviation of the controlled temperature distribution to the ideal desired temperature distribution for all locations and all times indicate that the adaptive schemes perform better than the PID scheme. It can be concluded that adaptive control yields improved performance if good a priori knowledge of the treated region tissue and perfusion region boundaries is available. While these control schemes were designed for eventual implementation on a scanned focused ultrasound hyperthermia treatment system, the techniques are applicable to any system with the capability to vary specific power with respect to location and with an unknown distributed energy sink proportional to the temperature elevation.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectThermotherapy.en_US
dc.subjectThermotherapy -- Equipment and supplies -- Automatic control.en_US
dc.titleAdaptive model-following control for hyperthermia treatment systems.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.identifier.oclc701248654en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberCetas, Thomas C.en_US
dc.contributor.committeememberPearlstein, Arne J.en_US
dc.contributor.committeememberHynynen, Kullervoen_US
dc.identifier.proquest8816315en_US
thesis.degree.disciplineAerospace and Mechanical Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
dc.description.noteDigitization note: pg. 20 missing from paper original.en
refterms.dateFOA2018-08-22T18:33:52Z
html.description.abstractThe purpose of this research was to develop three real-time adaptive temperature controllers for hyperthermia heating systems. Each scheme is made adaptive by using a transient Gaussian estimation routine to estimate the tissue blood perfusion and by then using these estimated values either in an optimizing routine, or in an observer, or in both. The optimizing routine uses a steady-state Gaussian estimation technique to optimize the power distribution until the best possible match is obtained between the steady-state temperatures predicted by a treatment model and a prespecified ideal temperature distribution. The observer uses a treatment model to control unmeasured locations. The first adaptive control scheme uses the optimizing routine alone, the second uses the observer alone and the third uses both the optimzing routine and observer. The performance of each of the adaptive control schemes is compared to a standard proportional-integral-derivative (PID) control scheme for one-dimensional simulations of typical treatments. Results comparing the deviation of the controlled temperature distribution to the ideal desired temperature distribution for all locations and all times indicate that the adaptive schemes perform better than the PID scheme. It can be concluded that adaptive control yields improved performance if good a priori knowledge of the treated region tissue and perfusion region boundaries is available. While these control schemes were designed for eventual implementation on a scanned focused ultrasound hyperthermia treatment system, the techniques are applicable to any system with the capability to vary specific power with respect to location and with an unknown distributed energy sink proportional to the temperature elevation.


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