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Perfect tracking for non-minimum phase systems with applications to biofuels from microalgae

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dc.contributor.authorBuehner, Michael R., author
dc.contributor.authorYoung, Peter M., advisor
dc.contributor.authorChong, Edwin Kah Pin, committee member
dc.contributor.authorScharf, Louis L., committee member
dc.contributor.authorAnderson, Charles W., committee member
dc.date.accessioned2007-01-03T05:45:57Z
dc.date.available2007-01-03T05:45:57Z
dc.date.issued2010
dc.descriptionDepartment Head: Anthony A. Maciejewski.
dc.description.abstractIn a causal setting, a closed-loop control system receives reference inputs (with no a priori knowledge) that it must track. For this setting, controllers are designed that provide both stability and performance (e.g., to meet tracking and disturbance rejection requirements). Often, feedback controllers are designed to satisfy weighted optimization criteria (e.g., weighted tracking error) that are later validated using test signals such as step responses and frequency sweeps. Feedforward controllers may be used to improve the response to measurable external disturbances (e.g., reference inputs). In this way, they can improve the closed-loop response; however, these approaches do not directly specify the closed-loop response. Two controller architectures are developed that allow for directly designing the nominal closed-loop response of non-minimum phase systems. These architectures classify both the signals that may be perfectly tracked by a non-minimum phase plant and the control signals that provide this perfect tracking. For these architectures, perfect tracking means that the feedback error is zero (for all time) in the nominal case (i.e., the plant model is exact) when there are no external disturbances. For the controllers presented here, parts of the feedforward controllers are based on the plant model, while a separate piece is designed to provide the desired level of performance. One of the potential limitations to these designs is that the actual performance will depend upon the quality of the model used. Robustness tools are developed that may be used to determine the expected performance for a given level of model uncertainty. These robustness tools may also be used to design the piece of the feedforward controller that provides performance. There is a tradeoff between model uncertainty and achievable performance. In general, more model uncertainty will result in less achievable performance. Another way to approach the issue of performance is to consider that a good model must either be known a priori or learned via adaptation. In the cases where a good model is difficult to determine a priori, adaptation may be used to improve the models in the feedforward controllers, which will, in turn, improve the performance of the overall control system. We show how adaptive feedforward architectures can improve performance for systems where the model is of limited accuracy. An example application of growing microalgae for biofuel production is presented. Microalgae have the potential to produce enough biofuels to meet the current US fuel demands; however, progress has been limited (in some part) due to a lack of appropriate models and controllers. In the work presented here, models are developed that may be used to monitor the productivity of microalgae inside a photobioreactor and to develop control algorithms. We use experimental data from a functional prototype photobioreactor to validate these models and to demonstrate the advantages of the advanced controller architectures developed here.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifier2010_Summer_Buehner_Michael.pdf
dc.identifierBuehner_colostate_0053A_10089.pdf
dc.identifierETDF2010100004ECEN
dc.identifier.urihttp://hdl.handle.net/10217/39322
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectrobust
dc.subjectmicroalgae
dc.subjectfeedforward
dc.subjectcontrol systems
dc.subjectbiofuels
dc.subjectadaptive
dc.subjectAdaptive control systems
dc.subjectFeedforward control systems
dc.subjectBiomass energy
dc.subjectMicroalgae -- Biotechnology
dc.titlePerfect tracking for non-minimum phase systems with applications to biofuels from microalgae
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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