Detalle del congreso

Autores: Alejandro Anderson; Alejandro González; Antonio Ferramosca; Ernesto Kofman.

Resumen: Recently, a Model Predictive Control (MPC) scheme suitable for closed-loop re-identication was proposed which solves, in a non-conservative form, thepotential con ict between the persistent excitation of the system and the stabilization.The idea is to use the concept of probabilistic invariance to dene a target set,and so to take advantage of the knowledge of the probabilistic distribution ofthe excitation signal to design a non-competitive two-objective MPC formulation.Although this proposal seems to work properly from an identication point of view(since uncorrelated output-input data are obtained), some theoretical properties of the formulation remains unexploited. In this work, new results are presented, focusing on the nite-time convergence to the target, which is necessary to start the second MPC objective of identication. Furthermore, several new simulation are developed to clearly show the new properties benets.

Tipo de reunión: Congreso.

Tipo de trabajo: Artículo Completo.

Producción: Extended MPC for closed-loop re-identification based on probabilistic invariant sets.

Reunión científica: XXVº Congreso Argentino de Control Automático (AADECA 2016).

Lugar: Buenos Aires.

Institución organizadora: AADECA.

Publicado: Sí

Lugar publicación: Buenos Aires

Mes de reunión: 11

Año: 2016.