Polynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parameters

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dc.contributor.authorBlanchard, Emmanuelen
dc.contributor.committeechairSandu, Adrianen
dc.contributor.committeecochairSandu, Corinaen
dc.contributor.committeememberLeo, Donald J.en
dc.contributor.committeememberBorggaard, Jeffrey T.en
dc.contributor.committeememberAhmadian, Mehdien
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2014-03-14T20:09:13Zen
dc.date.adate2010-05-03en
dc.date.available2014-03-14T20:09:13Zen
dc.date.issued2010-03-26en
dc.date.rdate2010-05-03en
dc.date.sdate2010-04-09en
dc.description.abstractMechanical systems operate under parametric and external excitation uncertainties. The polynomial chaos approach has been shown to be more efficient than Monte Carlo approaches for quantifying the effects of such uncertainties on the system response. This work uses the polynomial chaos framework to develop new methodologies for the simulation, parameter estimation, and control of mechanical systems with uncertainty. This study has led to new computational approaches for parameter estimation in nonlinear mechanical systems. The first approach is a polynomial-chaos based Bayesian approach in which maximum likelihood estimates are obtained by minimizing a cost function derived from the Bayesian theorem. The second approach is based on the Extended Kalman Filter (EKF). The error covariances needed for the EKF approach are computed from polynomial chaos expansions, and the EKF is used to update the polynomial chaos representation of the uncertain states and the uncertain parameters. The advantages and drawbacks of each method have been investigated. This study has demonstrated the effectiveness of the polynomial chaos approach for control systems analysis. For control system design the study has focused on the LQR problem when dealing with parametric uncertainties. The LQR problem was written as an optimality problem using Lagrange multipliers in an extended form associated with the polynomial chaos framework. The solution to the Hâ problem as well as the H2 problem can be seen as extensions of the LQR problem. This method might therefore have the potential of being a first step towards the development of computationally efficient numerical methods for Hâ design with parametric uncertainties. I would like to gratefully acknowledge the support provided for this work under NASA Grant NNL05AA18A.en
dc.description.degreePh. D.en
dc.identifier.otheretd-04092010-000752en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-04092010-000752/en
dc.identifier.urihttp://hdl.handle.net/10919/26727en
dc.publisherVirginia Techen
dc.relation.haspartBlanchard_ED_D_2010.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectCollocationen
dc.subjectPolynomial Chaosen
dc.subjectParametric Uncertaintyen
dc.subjectParameter Estimationen
dc.subjectExtended Kalman Filter (EKF)en
dc.subjectBayesian Estimationen
dc.subjectVehicle Dynamicsen
dc.subjectControl Designen
dc.subjectRobust Controlen
dc.subjectLQRen
dc.titlePolynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parametersen
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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