Inference of candidate loop performance and data filtering for switching supervisory control
✍ Scribed by Edoardo Mosca; Tommaso Agnoloni
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 292 KB
- Volume
- 37
- Category
- Article
- ISSN
- 0005-1098
No coin nor oath required. For personal study only.
✦ Synopsis
The paper studies the problem of inferring the performance of a linear feedback-loop consisting of an uncertain plant and a candidate controller from data taken from the same plant possibly driven by a di!erent controller. In such a context, a convenient tool to work with is a quantity called normalized discrepancy. This is a quadratic measure of mismatch between the loop made up by the unknown plant in feedback with the candidate controller and the nominal `tuned-loopa related to the same candidate controller. It is shown that discrepancy can be in principle obtained by resorting to the concept of a virtual reference, and conveniently computed in real-time by suitably "ltering an output prediction error. The latter result is of relevant practical value for on-line implementation and of paramount importance in switching supervisory control of uncertain plants, particularly in the case of a coarse candidate model distribution.