자료유형 | 학위논문 |
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서명/저자사항 | Advances in Nonlinear Model Predictive Control for Large-Scale Chemical Process Systems. |
개인저자 | Griffith, Devin Wade. |
단체저자명 | Carnegie Mellon University. Chemical Engineering. |
발행사항 | [S.l.]: Carnegie Mellon University., 2018. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
형태사항 | 154 p. |
기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
ISBN | 9780438200050 |
학위논문주기 | Thesis (Ph.D.)--Carnegie Mellon University, 2018. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Lorenz T. Biegler. |
요약 | Model 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 |
요약 | First, 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 |
요약 | Next, 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 |
요약 | Also, 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 |
요약 | Finally, 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 |
일반주제명 | Chemical engineering. |
언어 | 영어 |
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