On Improving the Efficiency of Modifier Adaptation via Directional Information

• Autores: Rodrigues, D.; Marchetti, Alejandro Gabriel; Bonvin, D.
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: ACTIVE SET; DOMINANT GRADIENTS; MODIFIER ADAPTATION; PLANT-MODEL MISMATCH; REAL-TIME OPTIMIZATION
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

In real-time optimization, the solution quality depends on the model ability to predict the plant Karush–Kuhn–Tucker (KKT) conditions. In the case of non-parametric plant-model mismatch, one can add input-affine modifiers to the model cost and constraints as is done in modifier adaptation (MA). These modifiers require estimating the plant cost and constraint gradients. This paper discusses two ways of reducing the number of input directions, thereby improving the efficiency of MA in practice. The first approach capitalizes on the knowledge of the active set to reduce the number of KKT conditions. The second approach determines the dominant gradients using sensitivity analysis. This way, MA reaches near plant optimality efficiently by adapting the first-order modifiers only along the dominant input directions. These approaches allow generating several alternative MA schemes, which are analyzed in terms of the number of degrees of freedom and compared in a simulated study of the Williams–Otto plant.
Fil: Rodrigues, D.. Instituto Superior Tecnico; Portugal
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: Bonvin, D.. Ecole Polytechnique Federale de Lausanne; Francia