Modifier Adaptation as a Feedback Control Scheme
| Autor: | Sean Costello, Alejandro Marchetti, T. de Avila Ferreira, Dominique Bonvin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Scheme (programming language)
PLANT-MODEL MISMATCH Computer science MODEL ADEQUACY General Chemical Engineering Feedback control MODEL ACCURACY 02 engineering and technology General Chemistry purl.org/becyt/ford/2.2 [https] 021001 nanoscience & nanotechnology Industrial and Manufacturing Engineering REAL-TIME OPTIMIZATION 020401 chemical engineering purl.org/becyt/ford/2 [https] Control theory MODIFIER ADAPTATION 0204 chemical engineering 0210 nano-technology Adaptation (computer science) computer CONSTRAINT ADAPTATION computer.programming_language |
| Zdroj: | CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET |
| Popis: | As a real-time optimization technique, modifier adaptation (MA) has gained much significance in recent years. This is mainly due to the fact that MA can deal explicitly with structural plant-model mismatch and unknown disturbances. MA is an iterative technique that is ideally suited to real-life applications. Its two main features are the way measurements are used to correct the model and the role played by the model in actually computing the next inputs. This paper analyzes these two features and shows that, although MA computes the next inputs via numerical optimization, it can be viewed as a feedback control scheme, that is, optimization implements tracking of the plant Karush-Kuhn-Tucker (KKT) conditions. As a result, the role of the model is downplayed to the point that model accuracy is not an important issue. The key issues are gradient estimation and model adequacy, the latter requiring that the model possesses the correct curvature of the cost function at the plant optimum. The main role of optimization is to identify the proper set of controlled variables (the active constraints and reduced gradients) as these might change with the operating point and disturbances. Thanks to this reduced requirement on model accuracy, MA is ideally suited to drive real-life processes to optimality. This is illustrated through two experimental systems with very different optimization features, namely, a commercial fuel-cell system and an experimental kite setup for harnessing wind energy. Fil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina Fil: de Avila Ferreira, T.. Ecole Polytechnique Federale de Lausanne; Francia Fil: Costello, Sergio Gustavo. Ecole Polytechnique Federale de Lausanne; Francia Fil: Bonvin, Dominique. Ecole Polytechnique Federale de Lausanne; Francia |
| Databáze: | OpenAIRE |
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