The objective of this note is to introduce a novel data-driven iterative linear quadratic control method for solving a class of nonlinear optimal tracking problems. Specifically, an algorithm is proposed to approximate the Q-factors arising from linear quadratic stochastic optimal tracking problems. This algorithm is then coupled with iterative linear quadratic methods for determining local solutions to nonlinear optimal tracking problems in a purely data-driven setting. Simulation results highlight the potential of this method for field applications.
An iterative data-driven linear quadratic method to solve nonlinear discrete-time tracking problems
Incremona, Gian Paolo;
2021-01-01
Abstract
The objective of this note is to introduce a novel data-driven iterative linear quadratic control method for solving a class of nonlinear optimal tracking problems. Specifically, an algorithm is proposed to approximate the Q-factors arising from linear quadratic stochastic optimal tracking problems. This algorithm is then coupled with iterative linear quadratic methods for determining local solutions to nonlinear optimal tracking problems in a purely data-driven setting. Simulation results highlight the potential of this method for field applications.File in questo prodotto:
File | Dimensione | Formato | |
---|---|---|---|
discrete_tracking_learning_TAC_original.pdf
Accesso riservato
Descrizione: Articolo principale
:
Publisher’s version
Dimensione
537.34 kB
Formato
Adobe PDF
|
537.34 kB | Adobe PDF | Visualizza/Apri |
discrete_tracking_learning_TAC_pub.pdf
accesso aperto
Descrizione: Articolo principale
:
Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione
1.48 MB
Formato
Adobe PDF
|
1.48 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.