Stabilizing dynamic state feedback controller synthesis: A reinforcement learning approach

Miguel A. Solis, Manuel Olivares, Héctor Allende

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

2 Citas (Scopus)

Resumen

State feedback controllers are appealing due to their structural simplicity. Nevertheless, when stabilizing a given plant, dynamics of this type of controllers could lead the static feedback gain to take higher values than desired. On the other hand, a dynamic state feedback controller is capable of achieving the same or even better performance by introducing additional parameters into the model to be designed. In this document, the Linear Quadratic Tracking problem will be tackled using a (linear) dynamic state feedback controller, whose parameters will be chosen by means of applying reinforcement learning techniques, which have been proved to be especially useful when the model of the plant to be controlled is unknown or inaccurate.

Idioma originalInglés
Páginas (desde-hasta)245-254
Número de páginas10
PublicaciónStudies in Informatics and Control
Volumen25
N.º2
EstadoPublicada - 1 ene. 2016
Publicado de forma externa

Áreas temáticas de ASJC Scopus

  • Informática (todo)
  • Ingeniería eléctrica y electrónica

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