1990 American Control Conference 1990
DOI: 10.23919/acc.1990.4791158
|View full text |Cite
|
Sign up to set email alerts
|

An Industrial Perspective on Control-Relevant Identification

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4

Citation Types

0
4
0

Year Published

1992
1992
2004
2004

Publication Types

Select...
3
2
1

Relationship

0
6

Authors

Journals

citations
Cited by 13 publications
(4 citation statements)
references
References 3 publications
0
4
0
Order By: Relevance
“…Rivera and Gaikwad 9 have discussed the modeling issues for achieving satisfactory closed-loop performance using a modelbased controller. Rivera and co-workers 1, [5][6][7]9 have focused on prefilter-based methods for the CRI of SISO systems for feedback and combined feed-forward/ feedback control design and have also discussed issues such as input design. 8,10,12 Kwok and Shah 13 proposed a method wherein the control-relevant model is estimated with a terminal matching condition that makes computation easy in a generalized predictive control scheme.…”
Section: Introductionmentioning
confidence: 99%
See 2 more Smart Citations
“…Rivera and Gaikwad 9 have discussed the modeling issues for achieving satisfactory closed-loop performance using a modelbased controller. Rivera and co-workers 1, [5][6][7]9 have focused on prefilter-based methods for the CRI of SISO systems for feedback and combined feed-forward/ feedback control design and have also discussed issues such as input design. 8,10,12 Kwok and Shah 13 proposed a method wherein the control-relevant model is estimated with a terminal matching condition that makes computation easy in a generalized predictive control scheme.…”
Section: Introductionmentioning
confidence: 99%
“…The branch of system identification that deals with such strategies is called control-relevant identification (CRI). The focus of research publications by Rivera and co-workers, Kwok and Shah, Shook et al, and Van den Hof and Schrama , is mainly centered on CRI. In traditional system identification, the objective is the minimization of bias and variance error of the estimated model, whereas in CRI, the aim is to minimize the model−plant mismatch in the frequency band that is most relevant from a closed-loop-performance viewpoint.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…In the interest of reducing conservatism in the plant uncertainty description, there have been recent efforts aimed at characterizing the plant set using system identification techniques [5][6] [14] [15][17] [20][22] [23] [28]. In the case that experimental input/output data is available from the system, this requires characterizing the set of plants which are consistent with (or equivalently, can't be discounted based on), the data.…”
Section: Introductionmentioning
confidence: 99%