Self-optimizing control – A survey

Show simple item record Jäschke, Johannes Cao, Yi Kariwala, Vinay 2017-04-06T10:07:38Z 2017-04-06T10:07:38Z 2017-04-04
dc.identifier.citation Jäschke J, Cao Y, Kariwala V, Self-optimizing control – A survey, Annual Reviews in Control, Vol. 43, 2017, pp. 199-223 en_UK
dc.identifier.issn 1367-5788
dc.description.abstract Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by the fact that an economic objective function is adopted as a selection criterion. The aim is to systematically select the controlled variables such that by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. If a selection leads to an acceptable economic loss compared to perfectly optimal operation then the chosen control structure is referred to as “self-optimizing”. In this comprehensive survey on methods for finding self-optimizing controlled variables we summarize the progress made during the last fifteen years. In particular, we present brute-force methods, local methods based on linearization, data and regression based methods, and methods for finding nonlinear controlled variables for polynomial systems. We also discuss important related topics such as handling changing active constraints. Finally, we point out open problems and directions for future research. en_UK
dc.language.iso en en_UK
dc.publisher Elsevier Science B.V., Amsterdam. en_UK
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Self-optimizing control en_UK
dc.subject Control structure selection en_UK
dc.subject Controlled variables en_UK
dc.subject Plant-wide control en_UK
dc.title Self-optimizing control – A survey en_UK
dc.type Article (Literature review, Editorial) en_UK

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