Title:
A Study on Architecture, Algorithms, and Applications of Approximate Dynamic Programming Based Approach to Optimal Control

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dc.contributor.advisor Lee, Jay H.
dc.contributor.author Lee, Jong Min en_US
dc.contributor.committeeMember Ahmed, Shabbir
dc.contributor.committeeMember Gallivan, Martha
dc.contributor.committeeMember Realff, Matthew J.
dc.contributor.committeeMember Schork, F. Joseph
dc.contributor.department Chemical Engineering en_US
dc.date.accessioned 2005-03-02T22:22:47Z
dc.date.available 2005-03-02T22:22:47Z
dc.date.issued 2004-07-12 en_US
dc.description.abstract This thesis develops approximate dynamic programming (ADP) strategies suitable for process control problems aimed at overcoming the limitations of MPC, which are the potentially exorbitant on-line computational requirement and the inability to consider the future interplay between uncertainty and estimation in the optimal control calculation. The suggested approach solves the DP only for the state points visited by closed-loop simulations with judiciously chosen control policies. The approach helps us combat a well-known problem of the traditional DP called 'curse-of-dimensionality,' while it allows the user to derive an improved control policy from the initial ones. The critical issue of the suggested method is a proper choice and design of function approximator. A local averager with a penalty term is proposed to guarantee a stably learned control policy as well as acceptable on-line performance. The thesis also demonstrates versatility of the proposed ADP strategy with difficult process control problems. First, a stochastic adaptive control problem is presented. In this application an ADP-based control policy shows an "active" probing property to reduce uncertainties, leading to a better control performance. The second example is a dual-mode controller, which is a supervisory scheme that actively prevents the progression of abnormal situations under a local controller at their onset. Finally, two ADP strategies for controlling nonlinear processes based on input-output data are suggested. They are model-based and model-free approaches, and have the advantage of conveniently incorporating the knowledge of identification data distribution into the control calculation with performance improvement. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 4367104 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/5048
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Penalty function en_US
dc.subject Reinforcement learning
dc.subject Neuro-dynamic programming
dc.subject Model predictive control
dc.subject Function approximation
dc.title A Study on Architecture, Algorithms, and Applications of Approximate Dynamic Programming Based Approach to Optimal Control en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename School of Chemical and Biomolecular Engineering
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication 6cfa2dc6-c5bf-4f6b-99a2-57105d8f7a6f
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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