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Advances in Nonlinear Model Predictive Control for Large-Scale Chemical Process Systems

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개인저자Griffith, Devin Wade.
단체저자명Carnegie Mellon University. Chemical Engineering.
서명/저자사항Advances in Nonlinear Model Predictive Control for Large-Scale Chemical Process Systems.
발행사항[S.l.] : Carnegie Mellon University., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항154 p.
소장본 주기School code: 0041.
ISBN9780438200050
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Lorenz T. Biegler.
요약Model predictive control is an optimization based form of control that is commonly used in the chemical industry due to its natural handling of multiple-input-multiple-output systems and inequality constraints. Nonlinear model predictive control
요약First, we address the issues of NMPC applied with plant-model mismatch. Robust NMPC methods tend to be computationally expensive or lead to conservatism in performance. Therefore, we propose a framework by which NMPC may be given a straightforwa
요약Next, we consider the computation of terminal conditions (regions and costs). Terminal conditions are a critical aspect of NMPC formulations that is closely intertwined with stability of the controller and feasibility of the optimization problem
요약Also, we consider the application of economic NMPC (eNMPC) to large-scale systems. We propose an eNMPC scheme which enforces stability though a stabilizing constraint, a method which we deem eNMPC-sc. We show that eNMPC-sc is input-to-state prac
요약Finally, we consider the selection of the predictive horizon length. In particular, we consider a method for updating horizon lengths online that we call adaptive horizon NMPC (AH-NMPC). We show an algorithm utilizing NLP sensitivity calculation
일반주제명Chemical engineering.
언어영어
기본자료 저록Dissertation Abstracts International79-12B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14999539

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