Parametric Optimal Design Of Uncertain Dynamical Systems

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dc.contributor.authorHays, Joseph T.en
dc.contributor.committeecochairSandu, Adrianen
dc.contributor.committeecochairSandu, Corinaen
dc.contributor.committeecochairHong, Dennis W.en
dc.contributor.committeememberRoss, Shane D.en
dc.contributor.committeememberSouthward, Steve C.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2014-03-14T20:15:52Zen
dc.date.adate2011-09-02en
dc.date.available2014-03-14T20:15:52Zen
dc.date.issued2011-08-25en
dc.date.rdate2011-09-02en
dc.date.sdate2011-09-01en
dc.description.abstractThis research effort develops a comprehensive computational framework to support the parametric optimal design of uncertain dynamical systems. Uncertainty comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it; not accounting for uncertainty may result in poor robustness, sub-optimal performance and higher manufacturing costs. Contemporary methods for the quantification of uncertainty in dynamical systems are computationally intensive which, so far, have made a robust design optimization methodology prohibitive. Some existing algorithms address uncertainty in sensors and actuators during an optimal design; however, a comprehensive design framework that can treat all kinds of uncertainty with diverse distribution characteristics in a unified way is currently unavailable. The computational framework uses Generalized Polynomial Chaos methodology to quantify the effects of various sources of uncertainty found in dynamical systems; a Least-Squares Collocation Method is used to solve the corresponding uncertain differential equations. This technique is significantly faster computationally than traditional sampling methods and makes the construction of a parametric optimal design framework for uncertain systems feasible. The novel framework allows to directly treat uncertainty in the parametric optimal design process. Specifically, the following design problems are addressed: motion planning of fully-actuated and under-actuated systems; multi-objective robust design optimization; and optimal uncertainty apportionment concurrently with robust design optimization. The framework advances the state-of-the-art and enables engineers to produce more robust and optimally performing designs at an optimal manufacturing cost.en
dc.description.degreePh. D.en
dc.identifier.otheretd-09012011-162500en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09012011-162500/en
dc.identifier.urihttp://hdl.handle.net/10919/28850en
dc.publisherVirginia Techen
dc.relation.haspartHays_JT_T_2011.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectOrdinary Differential Equations (ODEs)en
dc.subjectTrajectory Planningen
dc.subjectMotion Planningen
dc.subjectGeneralized Polynomial Chaos (gPC)en
dc.subjectUncertainty Quantificationen
dc.subjectMulti-Objective Optimization (MOO)en
dc.subjectNonlinear Programming (NLP)en
dc.subjectDynamic Optimizationen
dc.subjectOptimal Controlen
dc.subjectRobust Design Optimization (RDO)en
dc.subjectCollocationen
dc.subjectUncertainty Apportionmenten
dc.subjectTolerance Allocationen
dc.subjectMultibody Dynamicsen
dc.subjectDifferential Algebraic Equations (DAEs)en
dc.titleParametric Optimal Design Of Uncertain Dynamical Systemsen
dc.typeDissertationen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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