.png){kind=icon}
Return
Multivariable Process Control: Decentralized, Decoupling, or Sparse?
.png){kind=icon}
PRE
AI
DOI：10.1021/ie901453z.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, a systematic approach is proposed to design PI/PID-based multivariable control systems in which the designs and analyses of decentralized, decoupling, and sparse control schemes are all treated under a unified framework. First, based oil the relative normalized gain array (RNGA), a best loop pairing is obtained using the RGA (relative gain array)-Nederlinski index (NI)-RNGA rules. The index matrix is then calculated, and the control Structure is determined according to a selection criterion. Finally, the selected loop controllers are independently designed based oil equivalent transfer functions. The effectiveness of the proposed design approach is verified by analysis of several multivariable industrial processes, demonstrating that the selected control Structure results in better overall system performance.
Keywords:
CONTROL-SYSTEM-DESIGN
RELAY-FEEDBACK
STRUCTURE SELECTION
RULES
MODEL
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.9
Papers:
4.0W
Citations:
9.6W
Organization
Cited Papers
ShareSaveShareSaveAlpha-bungarotoxin binding and central nervous system nicotinic acetylcholine receptors.png)
Neuroscience
NeuroscienceIF0
PREAI
ShareSaveShareSave
View more