Show simple item record

dc.contributor.advisorRichard D. Braatz.en_US
dc.contributor.authorShen, Dongying Erinen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Chemical Engineering.en_US
dc.date.accessioned2018-05-23T16:30:55Z
dc.date.available2018-05-23T16:30:55Z
dc.date.copyright2017en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/115701
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, February 2018.en_US
dc.descriptionCataloged from PDF version of thesis. "September 2017." Handwritten on title page "February 2018."en_US
dc.descriptionIncludes bibliographical references (pages 117-121).en_US
dc.description.abstractThe importance of taking model uncertainties into account during controller design is well established. Although this theory is well developed and quite mature, the worst-case uncertainty descriptions assumed in robust control formulations are incompatible with the uncertainty descriptions generated by commercial model identification software that produces time-invariant parameter uncertainties typically in the form of probability distribution functions. This doctoral thesis derives rigorous theory and algorithms for the optimal control of dynamical systems with time-invariant probabilistic uncertainties. The main contribution of this thesis is new feedback control design algorithms for linear time-invariant systems with time-invariant probabilistic parametric uncertainties and stochastic noise. The originally stochastic system of equations is transformed into an equivalent deterministic system of equations using polynomial chaos (PC) theory. In addition, the H2- and H[infinity symbol]-norms commonly used to describe the effect of stochastic noise on output are transformed such that the eventual closed-loop performance is insensitive to parametric uncertainties. A robustifying constant is used to enforce the closed-loop stability of the original system of equations. This approach results in the first PC-based feedback control algorithm with proven closed-loop stability, and the first PC-based feedback control formulation that is applicable to the design of fixed-order state and output feedback control designs. The numerical algorithm for the control design is formulated as optimization over bilinear matrix inequality (BMI) constraints, for which commercial software is available. The effectiveness of the approach is demonstrated in two case studies that include a continuous pharmaceutical manufacturing process. In addition to model uncertainties, chemical processes must operate within constraints, such as upper and lower bounds on the magnitude and rate of change of manipulated and/or output variables. The thesis also demonstrates an optimal feedback control formulation that explicitly addresses both constraints and time-invariant probabilistic parameter uncertainties for linear time-invariant systems. The H2-optimal feedback controllers designed using the BMI formulations are incorporated into a fast PC-based model predictive control (MPC) formulation. A numerical case study demonstrates the improved constraint satisfaction compared to past polynomial chaos-based formulations for model predictive control.en_US
dc.description.statementofresponsibilityby Dongying Erin Shen.en_US
dc.format.extent121 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectChemical Engineering.en_US
dc.titleOptimal control of dynamical systems with time-invariant probabilistic parametric uncertaintiesen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineering
dc.identifier.oclc1036985945en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record