| LDR | | 02633nam u200421 4500 |
| 001 | | 000000419280 |
| 005 | | 20190215163542 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438200050 |
| 035 | |
▼a (MiAaPQ)AAI10837090 |
| 035 | |
▼a (MiAaPQ)cmu:10268 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 660 |
| 100 | 1 |
▼a Griffith, Devin Wade. |
| 245 | 10 |
▼a Advances in Nonlinear Model Predictive Control for Large-Scale Chemical Process Systems. |
| 260 | |
▼a [S.l.]:
▼b Carnegie Mellon University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 154 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Lorenz T. Biegler. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Carnegie Mellon University, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0041. |
| 650 | 4 |
▼a Chemical engineering. |
| 690 | |
▼a 0542 |
| 710 | 20 |
▼a Carnegie Mellon University.
▼b Chemical Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0041 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999539
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |