Parametric Optimal Design Of Uncertain Dynamical Systems
| dc.contributor.author | Hays, Joseph T. | en |
| dc.contributor.committeecochair | Sandu, Adrian | en |
| dc.contributor.committeecochair | Sandu, Corina | en |
| dc.contributor.committeecochair | Hong, Dennis W. | en |
| dc.contributor.committeemember | Ross, Shane D. | en |
| dc.contributor.committeemember | Southward, Steve C. | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2014-03-14T20:15:52Z | en |
| dc.date.adate | 2011-09-02 | en |
| dc.date.available | 2014-03-14T20:15:52Z | en |
| dc.date.issued | 2011-08-25 | en |
| dc.date.rdate | 2011-09-02 | en |
| dc.date.sdate | 2011-09-01 | en |
| dc.description.abstract | This 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.degree | Ph. D. | en |
| dc.identifier.other | etd-09012011-162500 | en |
| dc.identifier.sourceurl | http://scholar.lib.vt.edu/theses/available/etd-09012011-162500/ | en |
| dc.identifier.uri | http://hdl.handle.net/10919/28850 | en |
| dc.publisher | Virginia Tech | en |
| dc.relation.haspart | Hays_JT_T_2011.pdf | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Ordinary Differential Equations (ODEs) | en |
| dc.subject | Trajectory Planning | en |
| dc.subject | Motion Planning | en |
| dc.subject | Generalized Polynomial Chaos (gPC) | en |
| dc.subject | Uncertainty Quantification | en |
| dc.subject | Multi-Objective Optimization (MOO) | en |
| dc.subject | Nonlinear Programming (NLP) | en |
| dc.subject | Dynamic Optimization | en |
| dc.subject | Optimal Control | en |
| dc.subject | Robust Design Optimization (RDO) | en |
| dc.subject | Collocation | en |
| dc.subject | Uncertainty Apportionment | en |
| dc.subject | Tolerance Allocation | en |
| dc.subject | Multibody Dynamics | en |
| dc.subject | Differential Algebraic Equations (DAEs) | en |
| dc.title | Parametric Optimal Design Of Uncertain Dynamical Systems | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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