.png){kind=icon}
Return
Data-driven predictive control for solid oxide fuel cells
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jprocont.2006.09.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper is concerned with predictive control of solid oxide fuel cells (SOFC) based on a benchmark model commonly studied in the dynamic SOFC modeling/control literature. It has been shown in previous studies that control of SOFC is challenging owing to the slow response and tight operating constraints. In this paper, we apply a data-driven predictive control approach to solving the control problem of the SOFC system. The predictive control applied is completely data based. In addition, unlike other data-driven predictive control designs, the proposed approach can deal with systems without complete on-line measurement of all output variables. Simulation results have demonstrated the feasibility of the control application. (C) 2006 Elsevier Ltd. All rights reserved.
Keywords:
SOFC
predictive control
subspace matrices
data-driven methods
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
3.5K
Citations:
7.3K
Organization
No organization information available
Cited Papers
Discovery and Validation of New Potential Biomarkers for Early Detection of Colon Cancer.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAIShareSaveAdenovirus-mediated gene transfer of fibromodulin inhibits neointimal hyperplasia in an organ culture model of human saphenous vein graft diseaseGene Therapy
Gene TherapyIF0
PREAI
ShareSave
Development of models for analyzing the load-following performance of microturbines and fuel cells
PREAI
ShareSave
View more