.png){kind=icon}
.png){kind=icon}
Return
On identification methods for direct data-driven controller tuning
.png){kind=icon}
OA
AI
DOI：10.1002/acs.1213.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In non-iterative data-driven controller tuning, a set of measured input/output data of the plant is used directly to identify the optimal controller that minimizes some control criterion. This approach allows the design of fixed-order controllers, but leads to an identification problem where the input is affected by noise, and not the output as in standard identification problems. Several solutions that deal with the effect of measurement noise in this specific identification problem have been proposed in the literature. The consistency and statistical efficiency of these methods are discussed in this paper and the performance of the different methods is compared. The conclusions offer a guideline on how to solve the data-driven controller tuning problem efficiently. Copyright (C) 2010 John Wiley & Sons, Ltd.
Keywords:
data-driven controller tuning
identification for control
undermodeling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.8
Papers:
2.6K
Citations:
3.6K