Self-optimizing control – A survey

Date published

2017-04-04

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Publisher

Elsevier Science B.V., Amsterdam.

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Type

Article (Literature review, Editorial)

ISSN

1367-5788

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Citation

Jäschke J, Cao Y, Kariwala V, Self-optimizing control – A survey, Annual Reviews in Control, Vol. 43, 2017, pp. 199-223

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.

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Software Description

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Github

Keywords

Self-optimizing control, Control structure selection, Controlled variables, Plant-wide control

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Attribution-NonCommercial-NoDerivatives 4.0 International

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