Exploiting local quasiconvexity for gradient estimation in modifier-adaptation schemes

Gene A. Bunin*, Gregory Francois, Dominique Bonvin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

A new approach for gradient estimation in the context of real-time optimization under uncertainty is proposed in this paper. While this estimation problem is often a difficult one, it is shown that it can be simplified significantly if an assumption on the local quasiconvexity of the process is made and the resulting constraints on the gradient are exploited. To do this, the estimation problem is formulated as a constrained weighted least-squares problem with appropriate choice of the weights. Two numerical examples illustrate the effectiveness of the proposed method in converging to the true process optimum, even in the case of significant measurement noise.

Original languageEnglish
Pages (from-to)2806-2811
Number of pages6
JournalProceedings of the American Conference
Publication statusPublished - 2012
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: 27 Jun 201229 Jun 2012

Fingerprint

Dive into the research topics of 'Exploiting local quasiconvexity for gradient estimation in modifier-adaptation schemes'. Together they form a unique fingerprint.

Cite this